Every category where a model named Milvus / 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%	12 of 24	36%	14	criticized challenger

Movement

This is the first edition on this tier, so no move can be computed for Milvus / 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 Milvus / 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	1	0	0	1
Gemini 3.5 Flash	0	0	0	0
Perplexity Sonar	0	0	0	0
Grok 4.1 Fast	1	0	2	3
Mistral Small	0	1	1	2
DeepSeek V4 Flash	0	1	2	3
Llama 4 Maverick	0	0	0	0
Qwen 3.7 Flash	1	0	0	1
Kimi K2	1	0	0	1
GLM 4.7 FlashX	2	0	0	2
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

Direct5 labelsNone

Paraphrase6 labelsNone

Comparative13 labels1not counted in share

Budget-constrained5 labels1

Scale-constrained1 labelNone

Negative1 labelNone

First choiceAlternativeMentionNegative31 labels in all, every segment counted; 1 of the 2 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.

“Massive scale, GPU accel, cost-efficient | Billions+ | Low storage; self-host free | Steeper ops curve self-hosted.” Grok 4.1 Fast · Vector databases · scale prompt · alternative

“Choose Milvus/Zilliz if: You are building an enterprise-grade application needing to index billions of vectors” Qwen 3.7 Flash · Vector databases · comparative prompt · alternative

“Choose Milvus (or Zilliz Cloud) if: You need to handle billions of vectors.” GLM 4.7 FlashX · Vector databases · comparative prompt · alternative

“Best For: Enterprise-scale applications requiring maximum scalability” Kimi K2 · Vector databases · 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.

“Avoid Milvus unless you're at massive scale (needs big engineering).” Grok 4.1 Fast · Vector databases · paraphrase prompt · hard negative

“Tradeoff: Significant operational complexity; you need engineers to configure and maintain it.” DeepSeek V4 Flash · Vector databases · budget prompt · soft negative

“like Milvus/Zilliz Cloud, require significant engineering resources to manage effectively” Mistral Small · Vector databases · negative prompt · soft negative

“Milvus/Zilliz: Overkill unless >50M vectors or GPU needs.” Grok 4.1 Fast · Vector databases · direct prompt · soft negative

Named alongside

The products named in the same answers as Milvus / Zilliz Cloud, over the 31 answers that named it. Took the first choice instead counts the answers where the other product was the first choice and Milvus / Zilliz Cloud was named but was not.

ProductSame answerTook the first choice insteadHead to head

Pinecone30 of 3115Not in the top three

Weaviate30 of 310Not in the top three

Qdrant29 of 319Not in the top three

pgvector26 of 318Not in the top three

Vespa10 of 310Not in the top three

Chroma8 of 310Not in the top three

ChromaDB8 of 310Not in the top three

Redis7 of 310Not in the top three

LanceDB6 of 310Not in the top three

Elasticsearch5 of 310Not 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 Milvus / 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 30 of the 31 answers that named Milvus / Zilliz Cloud and are not a share of its labels.

Domains cited

firecrawl.dev25

encore.dev23

dev.to14

marktechpost.com13

iternal.ai12

aloa.co9

liveblocks.io9

atlan.com8

zenml.io8

deploybase.ai7

128 of the 128 domain citations in answers naming Milvus / Zilliz Cloud came from somebody else's page.

Pages cited

firecrawl.dev/blog/best-vector-databases25

encore.dev/articles/best-vector-databases23

iternal.ai/insights/best-vector-databases-202612

dev.to/michaelaiglobal/beyond-pinecone-a-developers-deep-dive-into-the-top-10-vector-datab11

aloa.co/ai/comparisons/vector-database-comparison/best-enterprise-vector-databases9

liveblocks.io/blog/whats-the-best-vector-database-for-building-ai-products9

marktechpost.com/2026/05/10/best-vector-databases-in-2026-pricing-scale-limits-and-archite9

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

Pages are listed as the models cited them.

Names read as Milvus / Zilliz Cloud

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

Milvus/Zilliz 5Milvus/Zilliz Cloud 2Milvus / Zilliz 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 Milvus / 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 Milvus / 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 milvus.io 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.