IT AI Index

Index › Vendors › Scribe · September 2026 Edition

1 category · Named, not ranked

Scribe

1Judge labels

0First choices

1Negative 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. Scribe was named 1 time in Logs, where Datadog Log Management led with 20%. 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-market57 of 59

under the label threshold

Enterprisenot named

No model named it for this buyer.

In logs · 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 Scribe 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
Log management	IT operations and endpoint	0%	57 of 59	100%	1	under 10 labels · led by Datadog Log Management at 20%

Movement

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

Model	Negative	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	0	0
GLM 4.7 FlashX	1	1
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.

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.

“abandoned and is no longer supported” GLM 4.7 FlashX · Logs · negative prompt · hard negative

Named alongside

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

ProductSame answerTook the first choice insteadHead to head

Azure Log Analytics Agent1 of 10Not in the top three

Better Stack1 of 10Not in the top three

Coralogix1 of 10Not in the top three

Datadog Log Management1 of 10Not in the top three

Elastic Stack1 of 10Not in the top three

Graylog1 of 10Not in the top three

Logging Made Easy1 of 10Not in the top three

Splunk1 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 Scribe. 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 Scribe and are not a share of its labels.

Domains cited

aisle.com1

app.opencve.io1

aws.amazon.com1

cert.ssi.gouv.fr1

cloudzero.com1

cmustrudel.github.io1

community.splunk.com1

coralogix.com1

crowdstrike.com1

cvebrief.com1

Ten of the ten domain citations in answers naming Scribe came from somebody else's page.

Pages cited

aisle.com/blog/discovering-9-dos-vulnerabilities-in-elastic-beats1

app.opencve.io/cve1

aws.amazon.com/marketplace/reviews/reviews-list/prodview-jlaunompo5wbw1

cert.ssi.gouv.fr/avis/CERTFR-2023-AVI-09371

cloudzero.com/blog/splunk-cost-optimization1

cmustrudel.github.io/papers/icse2025abandonment.pdf1

community.splunk.com/t5/All-Apps-and-Add-ons/splunk-pricing-license-cost-scenario/m-p/46381

coralogix.com/blog/log-management-challenges1

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 Scribe'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 Scribe, 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 scribehow.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.