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Index › Products › vLLM · October 2026 Edition
1 category · Named, not ranked

vLLM

1Judge labels
0First choices
1Negative labels
1 / 14Models named it
1Category
October 2026 Edition. Every number here is derived from the raw labels under vendor table v2026-10.8, every buyer segment counted.
Standing
1 label, too few to rank
A product needs 10 labels in a category before a share or quadrant is stated. vLLM was named 1 time in ML platforms, where Azure Machine Learning led with 21%. The labels and the evidence are below, counted exactly.
By buyer segmentRead the same way at every buyer size.
In ml platforms · 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 vLLM 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.
CategoryFunctionShareRankNegative rateLabelsQuadrantSince September 2026
ML platformsData platform0%101 of 107100%1under 10 labels · led by Azure Machine Learning at 21%

Movement

This is the first edition on this tier, so no move can be computed for vLLM yet. From the next edition this section shows, per buyer segment and per category, whether its share moved by more than the measured noise floor.

By model

How each model treated vLLM across every prompt where it was named for a mid-market B2B company. Fourteen models, six prompts per category.
ShowHide
ModelFirst choiceAlternativeMentionNegativeLabels
Claude Haiku 4.500000
GPT-5.4 mini00000
Gemini 3.5 Flash00000
Perplexity Sonar00000
Grok 4.1 Fast00000
Mistral Small00000
DeepSeek V4 Flash00000
Llama 4 Maverick00000
Qwen 3.7 Flash00000
Kimi K200000
GLM 4.7 FlashX00000
MiniMax M2.500000
GPT-6 Luna00000
Muse Glimmer 30B00011

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
Comparative0 labelsNone
Budget-constrained0 labelsNone
Scale-constrained0 labelsNone
Negative1 labelNone
First choiceAlternativeMentionNegative1 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.

No positive label carried a quote.

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.

“40 distinct vulnerabilities (CVEs) across foundational tools and frameworks, including *MLflow*, *vLLM*, and *Hugging Face*” Muse Glimmer 30B · ML platforms · negative prompt · soft negative

Named alongside

The products named in the same answers as vLLM, over the 1 answer that named it. Took the first choice instead counts the answers where the other product was the first choice and vLLM was named but was not.
ShowHide
ProductSame answerTook the first choice insteadHead to head
Amazon SageMaker1 of 10Not among the top eight
Azure Machine Learning1 of 10Not among the top eight
ChatGPT1 of 10Not among the top eight
Claude1 of 10Not among the top eight
DeepSeek1 of 10Not among the top eight
Google Gemini1 of 10Not among the top eight
Google Vertex AI1 of 10Not among the top eight
Grok1 of 10Not among the top eight
Hugging Face1 of 10Not among the top eight
Le Chat1 of 10Not among the top eight
A head-to-head page exists where both products are among a category's top eight. 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 vLLM. A fact about retrieval, not a lever on the model.

Citations exist only for the models that return a source list, five of the fourteen in this edition, so these counts come from 1 of the 1 answers that named vLLM and are not a share of its labels.

Domains cited

21998286.fs1.hubspotusercontent-na1.net1
ar5iv.labs.arxiv.org1
arxiv.org1
blog.incogni.com1
dev.to1
divio.com1
helpnetsecurity.com1
labs.cloudsecurityalliance.org1
linkedin.com1
openagreements.org1

No domain is on file for vLLM, so its own site is not marked.

Pages cited

Pages are listed as the models cited them.

Follow vLLM

An email the morning each edition publishes: where this product moved, where it held, and by how much against the noise floor. One address, confirmed by a click; a stop link in every email.

Already following? Everything you follow, with a stop for each.

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 vLLM'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 vLLM, applied by version and listed in the change log; and a one-line description supplied by the vendor and marked as such.

What a new claim receivesHide what a new claim receives

A new claim receives the current edition's vendor brief for vLLM by email, built from the raw record of the edition. It shows:

  • where vLLM is named, by buyer and by framing, and which cells hold its first choices;
  • the claims the models make when they name it, ranked, with the strongest and the weakest quoted;
  • its vocabulary against the segment leader's, and the pages the models cited;
  • who was chosen in the answers that did not name vLLM, and every reason the record gives;
  • a battlecard for each top rival: the head-to-head split, why they win, and the reservation quoted against them;
  • one page of published figures cleared to show a buyer.

A verification link goes to your work email; an address at the vendor's own domain is approved on the spot, any other is reviewed by hand. Your email is never published. Claiming gives no say over labels, shares, verdicts or which quotes appear.