IT AI Index
Index Vendors › Palantir · September 2026 Edition
2 categories · Named, not ranked

Palantir

5Judge labels
0First choices
3Negative labels
5 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
4 labels, too few to rank
A product needs 10 labels in a category before a share or quadrant is stated. Palantir was named 4 times in ML platforms and 1 other category, where Azure Machine Learning led with 17%. 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 Palantir 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 rateLabelsQuadrant
ML platformsData platform0%80 of 9950%2under 10 labels · led by Azure Machine Learning at 17%
Threat intelligence platformsSecurity operations0%127 of 128100%2under 10 labels · led by NordStellar at 13%

Movement

This is the first edition on this tier, so no move can be computed for Palantir 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 Palantir across every prompt where it was named for a mid-market B2B company. Twelve models, six prompts per category.
ModelFirst choiceAlternativeMentionNegativeLabels
Claude Haiku 4.500000
GPT-5.4 mini00000
Gemini 3.5 Flash00000
Perplexity Sonar00000
Grok 4.1 Fast00000
Mistral Small00101
DeepSeek V4 Flash00011
Llama 4 Maverick00000
Qwen 3.7 Flash00000
Kimi K200011
GLM 4.7 FlashX00011
MiniMax M2.500000

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
Direct1 labelNone
Paraphrase0 labelsNone
Comparative1 labelNone
Budget-constrained0 labelsNone
Scale-constrained0 labelsNone
Negative3 labelsNone
First choiceAlternativeMentionNegative5 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.

“Significant human rights and privacy concerns... this poses reputational and ethical risks.” Kimi K2 · Threat intel · negative prompt · hard negative
“Verdict: Avoid or be extremely cautious if you are in a privacy-sensitive sector” GLM 4.7 FlashX · Threat intel · negative prompt · hard negative
“Long sales cycles, complex change management; geared to large enterprises, poor fit for small orgs” DeepSeek V4 Flash · ML platforms · negative prompt · soft negative

Named alongside

The products named in the same answers as Palantir, over the 5 answers that named it. Took the first choice instead counts the answers where the other product was the first choice and Palantir was named but was not.
ProductSame answerTook the first choice insteadHead to head
Databricks3 of 51Not in the top three
Amazon SageMaker3 of 50Not in the top three
Azure Machine Learning3 of 50Not in the top three
C3 AI3 of 50Not in the top three
Google Vertex AI3 of 50Not in the top three
DataRobot2 of 50Not in the top three
Dataiku2 of 50Not in the top three
Recorded Future2 of 50Not in the top three
SAS2 of 50Not in the top three
MISP1 of 51Not 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 Palantir. 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 5 of the 5 answers that named Palantir and are not a share of its labels.

Domains cited

g2.com3
research.oz.spotlightar.com3
aiadvisorypractice.com2
analyticsinsight.net2
checkthat.ai2
cloud.google.com2
cyware.com2
dagshub.com2
esecurityplanet.com2
guptadeepak.com2

Twenty-two of the twenty-two domain citations in answers naming Palantir came from somebody else's page.

Pages cited

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 Palantir'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 Palantir, 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 palantir.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.

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