Every category where a model named Zilliz Cloud 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%	9 of 24	12%	8	under 10 labels · led by Qdrant at 37%

Movement

This is the first edition on this tier, so no move can be computed for Zilliz Cloud 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 Zilliz Cloud 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	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	0	0	0
Llama 4 Maverick	0	0	0	0
Qwen 3.7 Flash	0	0	1	1
Kimi K2	1	1	0	2
GLM 4.7 FlashX	1	2	0	3
MiniMax M2.5	1	0	0	1

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 labels1

Paraphrase1 labelNone

Comparative2 labels1not counted in share

Budget-constrained3 labels1

Scale-constrained10 labelsNone

Negative8 labelsNone

First choiceAlternativeMentionNegative28 labels in all, every segment counted; 2 of the 3 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.

“| Large scale (hundreds of millions to billions) | Milvus, Qdrant Cloud, Zilliz Cloud |” GLM 4.7 FlashX · Vector databases · negative prompt · alternative

“Managed services like Pinecone, Zilliz Cloud, or Qdrant Cloud are popular” Kimi K2 · Vector databases · scale prompt · alternative

“| Dedicated managed | Pinecone, Qdrant Cloud, Zilliz Cloud |” MiniMax M2.5 · 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.

“The Trap: Milvus or Zilliz... notoriously difficult to tune and maintain” Qwen 3.7 Flash · Vector databases · negative prompt · soft negative

Named alongside

The products named in the same answers as Zilliz Cloud, over the 28 answers that named it. Took the first choice instead counts the answers where the other product was the first choice and Zilliz Cloud was named but was not.

ProductSame answerTook the first choice insteadHead to head

Pinecone28 of 288Not in the top three

Weaviate26 of 281Not in the top three

pgvector25 of 287Not in the top three

Qdrant23 of 284Not in the top three

Milvus23 of 280Not in the top three

Chroma9 of 280Not in the top three

Redis6 of 280Not in the top three

Elasticsearch5 of 280Not in the top three

MongoDB Atlas Vector Search5 of 280Not in the top three

LanceDB4 of 280Not 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 Zilliz Cloud. 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 23 of the 28 answers that named Zilliz Cloud and are not a share of its labels.

Domains cited

encore.dev14

firecrawl.dev13

actian.com12

iternal.ai10

pinecone.io9

atlan.com8

dev.to8

medium.com8

zilliz.comYour site7

sganalytics.com6

Eighty-eight of the ninety-five domain citations in answers naming Zilliz Cloud came from somebody else's page.

Pages cited

encore.dev/articles/best-vector-databases14

firecrawl.dev/blog/best-vector-databases13

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

actian.com/blog/databases/how-to-evaluate-vector-databases-in-20268

atlan.com/know/top-vector-databases-enterprise-ai8

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

g2.com/articles/best-vector-databases5

promptquorum.com/power-local-llm/enterprise-rag-vector-database-deployment-guide5

Pages are listed as the models cited them.

Names read as Zilliz Cloud

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

Zilliz 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 Zilliz Cloud'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 Zilliz Cloud, 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 zilliz.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.