Index › Vendors › GCP Vertex AI · September 2026 Edition

1 category · Named, not ranked

GCP Vertex AI

1Judge labels

0First choices

0Negative labels

1 of 12Models named it

1Category

September 2026 Edition. Every number here is derived from the raw labels under vendor table vv2026-09.2, 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. GCP Vertex AI was named 1 time in LLM gateways, where LiteLLM led with 38%. 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-market28 of 63

under the label threshold

Enterprisenot named

No model named it for this buyer.

In llm gateways · 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 GCP Vertex AI 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
LLM gateways and observability	Data platform	0%	28 of 63	0%	1	under 10 labels · led by LiteLLM at 38%

Movement

This is the first edition on this tier, so no move can be computed for GCP Vertex AI 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 GCP Vertex AI across every prompt where it was named for a mid-market B2B company. Twelve models, six prompts per category.

Model	Alternative	Labels
Claude Haiku 4.5	0	0
GPT-5.4 mini	0	0
Gemini 3.5 Flash	0	0
Perplexity Sonar	0	0
Grok 4.1 Fast	0	0
Mistral Small	0	0
DeepSeek V4 Flash	0	0
Llama 4 Maverick	0	0
Qwen 3.7 Flash	0	0
Kimi K2	1	1
GLM 4.7 FlashX	0	0
MiniMax M2.5	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

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.

“Azure OpenAI Service, AWS Bedrock, GCP Vertex AI (major cloud providers)” Kimi K2 · LLM gateways · negative 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.

No model argued against it.

Named alongside

The products named in the same answers as GCP Vertex AI, over the 1 answer that named it. Took the first choice instead counts the answers where the other product was the first choice and GCP Vertex AI was named but was not.

ProductSame answerTook the first choice insteadHead to head

Amazon Bedrock1 of 10Not in the top three

Anthropic1 of 10Not in the top three

Azure OpenAI Service1 of 10Not in the top three

LiteLLM1 of 10Not in the top three

OpenAI1 of 10Not 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 GCP Vertex AI. 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 1 of the 1 answers that named GCP Vertex AI and are not a share of its labels.

Domains cited

arxiv.org1

cybernews.com1

cybersecuritynews.com1

edpb.europa.eu1

gbhackers.com1

labs.cloudsecurityalliance.org1

me-en.kaspersky.com1

neuraltrust.ai1

quasa.io1

radar.offseq.com1

No domain is on file for GCP Vertex AI, so its own site is not marked.

Pages cited

arxiv.org/html/2505.01976v11

arxiv.org/html/2508.16765v11

cybernews.com/security/litellm-servers-exposed-require-no-password1

cybersecuritynews.com/cisa-warns-chinese-ai-firms/amp1

cybersecuritynews.com/hackers-exploit-litellm-admin-api-flaw/amp1

cybersecuritynews.com/hackers-target-ai-infrastructure/amp1

cybersecuritynews.com/litellm-flaws1

edpb.europa.eu/system/files/documents/2025-04/ai-privacy-risks-and-mitigations-in-llms.pdf1

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 GCP Vertex AI'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 GCP Vertex AI, 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 the vendor's own domain 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.