IT AI Index

Index › Vendors › Elasticsearch / OpenSearch · September 2026 Edition

Elasticsearch · 2 categories · Named, not ranked

Elasticsearch / OpenSearch

8Judge labels

0First choices

3Negative labels

4 of 12Models named it

2Categories

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

Standing

5 labels, too few to rank

A product needs 10 labels in a category before a share or quadrant is stated. Elasticsearch / OpenSearch was named 5 times in Vector databases and 1 other category, 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 businessnot named

No model named it for this buyer.

Mid-market15 of 24

under the label threshold

Enterprise34 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 Elasticsearch / OpenSearch 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%	15 of 24	25%	4	under 10 labels · led by Qdrant at 37%
NoSQL databases	Data platform	0%	30 of 72	0%	1	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 / OpenSearch 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 / OpenSearch 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	2	0	0	2
Gemini 3.5 Flash	0	1	0	1
Perplexity Sonar	0	0	0	0
Grok 4.1 Fast	0	0	0	0
Mistral Small	0	0	0	0
DeepSeek V4 Flash	0	1	0	1
Llama 4 Maverick	0	0	0	0
Qwen 3.7 Flash	0	0	0	0
Kimi K2	0	0	0	0
GLM 4.7 FlashX	0	0	1	1
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

Direct0 labelsNone

Paraphrase0 labelsNone

Comparative6 labelsNone

Budget-constrained0 labelsNone

Scale-constrained2 labelsNone

Negative0 labelsNone

First choiceAlternativeMentionNegative8 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.

“Choose Elasticsearch/OpenSearch if you already use them heavily for text search, logs, or analytics” GPT-5.4 mini · Vector databases · comparative prompt · alternative

“Need search/log analytics → Elasticsearch/OpenSearch” GPT-5.4 mini · 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.

“not ideal as a primary vector store at extreme scale” GLM 4.7 FlashX · Vector databases · comparative prompt · soft negative

Named alongside

The products named in the same answers as Elasticsearch / OpenSearch, over the 8 answers that named it. Took the first choice instead counts the answers where the other product was the first choice and Elasticsearch / OpenSearch was named but was not.

ProductSame answerTook the first choice insteadHead to head

Pinecone7 of 82Not in the top three

Qdrant7 of 80Not in the top three

Weaviate7 of 80Not in the top three

pgvector5 of 83Not in the top three

Milvus5 of 80Not in the top three

MongoDB Atlas Vector Search5 of 80Not in the top three

Redis5 of 80Not in the top three

LanceDB2 of 80Not in the top three

Milvus / Zilliz Cloud2 of 80Not in the top three

Vespa2 of 80Not 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 / OpenSearch. 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 4 of the 8 answers that named Elasticsearch / OpenSearch and are not a share of its labels.

Domains cited

pinecone.io3

dev.to2

docs.weaviate.io2

encore.dev2

firecrawl.dev2

milvus.io2

strapi.io2

1bench.dev1

4xxi.com1

acecloud.ai1

Eighteen of the eighteen domain citations in answers naming Elasticsearch / OpenSearch came from somebody else's page.

Pages cited

encore.dev/articles/best-vector-databases2

firecrawl.dev/blog/best-vector-databases2

milvus.io/docs2

pinecone.io/compare2

strapi.io/blog/best-vector-databases-ai-applications2

1bench.dev/databases/vector1

4xxi.com/articles/vector-database-comparison1

acecloud.ai/blog/best-vector-databases-for-multimodal-genai1

Pages are listed as the models cited them.

Names read as Elasticsearch / OpenSearch

What the judge wrote, as written, with how often. The vendor table decides that these count as Elasticsearch / OpenSearch; a claim can dispute any of them.

Elasticsearch/OpenSearch 1

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 / OpenSearch'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 / OpenSearch, 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 elastic.co 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.