IT AI Index
Index Vendors › AWS X-Ray · September 2026 Edition
AWS · 3 categories · Named, not ranked

AWS X-Ray

5Judge labels
0First choices
2Negative labels
3 of 12Models named it
3Categories
September 2026 Edition. Every number here is derived from the raw labels under vendor table vv2026-09.2, every buyer segment counted.
Standing
3 labels, too few to rank
A product needs 10 labels in a category before a share or quadrant is stated. AWS X-Ray was named 3 times in APM and 2 other categories, where New Relic led with 54%. The labels and the evidence are below, counted exactly.
By buyer segmentRead the same way at every buyer size.
In apm · 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 AWS X-Ray 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
Application performance monitoringIT operations and endpoint0%50 of 700%1under 10 labels · led by New Relic at 54%
Error and crash monitoringDeveloper platform0%31 of 44100%1under 10 labels · led by Sentry at 82%
Observability platformsIT operations and endpoint0%29 of 630%1under 10 labels · led by Grafana at 29%

Movement

This is the first edition on this tier, so no move can be computed for AWS X-Ray 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 AWS X-Ray 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 Small00000
DeepSeek V4 Flash00000
Llama 4 Maverick00000
Qwen 3.7 Flash00112
Kimi K200000
GLM 4.7 FlashX01001
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
Direct2 labelsNone
Paraphrase0 labelsNone
Comparative0 labelsNone
Budget-constrained1 labelNone
Scale-constrained0 labelsNone
Negative2 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.

“AWS CloudWatch + X-Ray” GLM 4.7 FlashX · Observability · budget 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.

“extracting that historical data out of AWS X-Ray becomes technically difficult and expensive” Qwen 3.7 Flash · Error monitoring · negative prompt · soft negative

Named alongside

The products named in the same answers as AWS X-Ray, over the 5 answers that named it. Took the first choice instead counts the answers where the other product was the first choice and AWS X-Ray was named but was not.
ProductSame answerTook the first choice insteadHead to head
Datadog5 of 51Not in the top three
New Relic4 of 50Not in the top three
Elastic Stack3 of 50Not in the top three
Splunk3 of 50Not in the top three
Grafana2 of 51Not in the top three
Sentry2 of 51Not in the top three
Azure Monitor2 of 50Not in the top three
CloudWatch2 of 50Not in the top three
Dynatrace2 of 50Not in the top three
Rollbar2 of 50Not 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 AWS X-Ray. 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 5 answers that named AWS X-Ray and are not a share of its labels.

Names read as AWS X-Ray

What the judge wrote, as written, with how often. The vendor table decides that these count as AWS X-Ray; a claim can dispute any of them.
X-Ray 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 AWS X-Ray'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 AWS X-Ray, 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.

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