Every category where a model named Elasticsearch 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%	23 of 24	40%	5	under 10 labels · led by Qdrant at 37%
NoSQL databases	Data platform	0%	47 of 72	50%	4	under 10 labels · led by MongoDB at 59%

Movement

This is the first edition on this tier, so no move can be computed for Elasticsearch 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 Elasticsearch 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	0	0	0
Gemini 3.5 Flash	0	0	0	0
Perplexity Sonar	0	0	1	1
Grok 4.1 Fast	0	1	0	1
Mistral Small	1	0	0	1
DeepSeek V4 Flash	1	0	1	2
Llama 4 Maverick	0	0	0	0
Qwen 3.7 Flash	0	1	1	2
Kimi K2	0	0	0	0
GLM 4.7 FlashX	0	1	1	2
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

Direct4 labelsNone

Paraphrase4 labelsNone

Comparative11 labelsNone

Budget-constrained0 labelsNone

Scale-constrained4 labelsNone

Negative15 labelsNone

First choiceAlternativeMentionNegative38 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 want a unified search experience, go with Elasticsearch.” Mistral Small · Vector databases · paraphrase prompt · alternative

“Full-text search & analytics → Elasticsearch” DeepSeek V4 Flash · NoSQL · comparative 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 / Elasticsearch with vector support | When your app is mostly semantic retrieval and not document/JSON or text-search centric” Perplexity Sonar · Vector databases · negative prompt · soft negative

“Risk Level: Medium-High Elasticsearch is often used for search capabilities but is frequently misconfigured.” GLM 4.7 FlashX · NoSQL · negative prompt · soft negative

“weren't designed for it from the ground up — watch memory consumption... limited algorithm support” DeepSeek V4 Flash · Vector databases · negative prompt · soft negative

“maintaining a self-hosted cluster of Apache Cassandra or Elasticsearch manually is dangerous” Qwen 3.7 Flash · NoSQL · scale prompt · soft negative

Named alongside

The products named in the same answers as Elasticsearch, over the 38 answers that named it. Took the first choice instead counts the answers where the other product was the first choice and Elasticsearch was named but was not.

ProductSame answerTook the first choice insteadHead to head

Pinecone22 of 389Not in the top three

Weaviate22 of 381Not in the top three

pgvector21 of 385Not in the top three

Qdrant20 of 383Not in the top three

Redis19 of 381Not in the top three

Milvus18 of 381Not in the top three

Chroma10 of 381Not in the top three

Vespa8 of 380Not in the top three

MongoDB7 of 382Not in the top three

Couchbase6 of 380Not 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 Elasticsearch. 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 24 of the 38 answers that named Elasticsearch and are not a share of its labels.

Domains cited

encore.dev13

firecrawl.dev13

atlan.com10

dev.to10

iternal.ai10

g2.com9

medium.com9

aloa.co7

reddit.com7

redis.io6

Ninety-four of the ninety-four domain citations in answers naming Elasticsearch came from somebody else's page.

Pages cited

encore.dev/articles/best-vector-databases13

firecrawl.dev/blog/best-vector-databases13

atlan.com/know/top-vector-databases-enterprise-ai10

iternal.ai/insights/best-vector-databases-202610

g2.com/articles/best-vector-databases8

aloa.co/ai/comparisons/vector-database-comparison/best-enterprise-vector-databases7

medium.com/@balarampanda.ai/top-vector-databases-for-enterprise-ai-in-2025-complete-select6

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

Pages are listed as the models cited 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 Elasticsearch'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 Elasticsearch, 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 elasticsearch.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.