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Index › IT › Products › MongoDB Atlas Vector Search · November 2026 Edition

Vector databases · Ranked

MongoDB Atlas Vector Search

MongoDB Atlas Vector Search is a vector search capability from MongoDB for building applications with vector search, hybrid search, and generative AI on the operational data already stored in MongoDB · mongodb.com ↗

#6Best rank (enterprise)

11/14Models named it

3First choices

2Warnings

November 2026 Edition · all company sizes counted.

Ranked inVector databases #6 enterprise

What AI models recommend

Read the same way at every company size.

The standing depends on who is buying. Every model is asked the same questions for each company size.

Small business

Asked for a small B2B company

Thin evidence 5 labels, under the 10-label minimum, so not ranked

pgvector is first at 35%.

Mid-market

Asked for a mid-market B2B company

Thin evidence 7 labels, under the 10-label minimum, so not ranked

Qdrant is first at 34%. 0 of 14 models made it the first choice when asked directly.

Enterprise

Asked for an enterprise B2B company

#6 of 68 named for enterprise

Pinecone is first at 45%.

Why models recommend it

“Best for: Companies already invested in PostgreSQL or MongoDB.”Qwen 3.7 Flash · Paraphrase question · alternative

“genuinely competitive for mid-scale RAG”Claude Haiku 4.5 · Direct question · alternative

What gives them pause

“Avoid heavier enterprise options like Elasticsearch/OpenSearch or MongoDB Atlas Vector Search”Grok 4.20 · Paraphrase question · warning

Also on the shortlist

The products named in the same answers as MongoDB Atlas Vector Search, over the 27 answers that named it, and how often each was the first choice instead.

Product	Named together	Picked instead
Pinecone	26	10	View →
pgvector	24	6	View →
Qdrant	23	7	View →
Weaviate	23	2	View →
Milvus / Zilliz Cloud	11	2	View →
Milvus	11	1	View →
Elasticsearch / OpenSearch	7	1	View →
Chroma	7	0	View →
Elasticsearch	7	0	View →
Azure AI Search	6	0	View →

A side-by-side page exists where both products are among a category's top eight.

At a glance

Stated on MongoDB Atlas Vector Search's own site, not checked by Gane.

For: teams
Free plan or trial: Get started for free in minutes
Hosting: 125+ regions worldwide
Customers named: Novo Nordisk
Also from MongoDB: MongoDB Atlas, MongoDB Atlas Vector

Visit mongodb.com ↗

How this is measured

Fourteen AI models from 12 labs are asked the same buying questions six ways, for three company sizes. Standings refresh every edition.

Gane sells AI visibility software. No company can pay to change a result.

Read the method →

Sources returned with these answers

The sites and pages the answers that named MongoDB Atlas Vector Search came back with. A fact about retrieval, not a cause of the answer.

Fourteen of the fourteen models returned a source list in this edition: five return the pages they cited and nine the search results they consulted. These counts come from 26 of the 27 answers that named MongoDB Atlas Vector Search and are not a share of its labels.

Domains cited

github.com14

dev.to9

linkedin.com8

g2.com7

atlan.com6

ciopages.com6

milvus.io6

mongodb.comIts own site6

pureinsights.com6

sganalytics.com6

Sixty-eight of the seventy-four domain citations in answers naming MongoDB Atlas Vector Search came from a third party's page.

Pages cited

g2.com/articles/best-vector-databases7

atlan.com/know/top-vector-databases-enterprise-ai6

ciopages.com/buyer-guides/vector-database6

pureinsights.com/blog/2024/comparing-vector-search-solutions-20246

sganalytics.com/blog/top-vector-databases-enterprise-ai6

liveblocks.io/blog/whats-the-best-vector-database-for-building-ai-products5

github.com/exploreborders/RAG_Architecture/blob/main/docs/3-technical/vector-databases.md4

milvus.io/blog/choosing-the-right-vector-database-for-your-ai-apps.md4

Pages are listed as the models cited them.

By model

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

Model	Alternative	Mention	Warning	Labels
Claude Haiku 4.5	1	0	0	1
GPT-5.4 mini	0	0	0	0
Gemini 3.5 Flash	0	0	0	0
Perplexity Sonar	0	0	0	0
Grok 4.20	0	1	1	2
Mistral Small	0	1	0	1
DeepSeek V4 Flash	0	2	0	2
Llama 4 Maverick	0	0	0	0
Qwen 3.7 Flash	1	0	0	1
Kimi K2	0	0	0	0
GLM 4.7 FlashX	0	0	0	0
MiniMax M2.5	0	0	0	0
GPT-6 Luna	0	0	0	0
Muse Glimmer 30B	0	0	0	0