IT AI Index
Index › Vendors › Confident AI · September 2026 Edition
1 category · Named, not ranked

Confident AI

9Judge labels
2First choices
0Negative labels
6 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
2 labels, too few to rank
A product needs 10 labels in a category before a share or quadrant is stated. Confident AI was named 2 times 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 business21 of 44
0%1 label
under the label threshold
Mid-market18 of 63
0%2 labels
under the label threshold
Enterprise7 of 62
4%5 labels
under the label threshold
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 Confident 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.
CategoryFunctionShareRankNegative rateLabelsQuadrant
LLM gateways and observabilityData platform0%18 of 630%2under 10 labels · led by LiteLLM at 38%

Movement

This is the first edition on this tier, so no move can be computed for Confident 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 Confident AI 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 Sonar01001
Grok 4.1 Fast00000
Mistral Small00000
DeepSeek V4 Flash00000
Llama 4 Maverick00000
Qwen 3.7 Flash00000
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
Direct0 labelsNone
Paraphrase8 labels2
Comparative0 labelsNone
Budget-constrained0 labelsNone
Scale-constrained1 labelNone
Negative0 labelsNone
First choiceAlternativeMentionNegative9 labels in all, every segment counted; 2 of the 2 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.

“Pair whichever you choose with Confident AI or Langfuse for evaluation and regression testing” GLM 4.7 FlashX · LLM gateways · paraphrase prompt · alternative
“Choose Confident AI if your main concern is *whether outputs are actually good*” Perplexity Sonar · LLM gateways · paraphrase 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 Confident AI, over the 9 answers that named it. Took the first choice instead counts the answers where the other product was the first choice and Confident AI was named but was not.
ProductSame answerTook the first choice insteadHead to head
Langfuse6 of 91Not in the top three
Braintrust4 of 92Not in the top three
LangSmith3 of 90Not in the top three
Nexos.ai2 of 91Not in the top three
Portkey2 of 91Not in the top three
Arize AI2 of 90Not in the top three
Datadog2 of 90Not in the top three
Datadog LLM Observability2 of 90Not in the top three
Dynatrace2 of 90Not in the top three
Helicone2 of 90Not 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 Confident 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 9 of the 9 answers that named Confident AI and are not a share of its labels.

Domains cited

confident-ai.comYour site9
braintrust.dev5
galileo.ai5
nexos.ai5
fast.io3
fastrouter.ai3
gbhackers.com3
openobserve.ai3
respan.ai3
signoz.io3

Thirty-three of the forty-two domain citations in answers naming Confident AI came from somebody else's page.

Pages cited

confident-ai.com/knowledge-base/compare/best-enterprise-llm-observability-tools8
nexos.ai/blog/llm-observability-tools5
braintrust.dev/articles/best-llm-routers-20264
confident-ai.com/knowledge-base/compare/10-llm-observability-tools-to-evaluate-and-monitor4
galileo.ai/blog/best-llm-monitoring-solutions-enterprise4
braintrust.dev/articles/best-llm-gateways-observability-20263
fast.io/resources/top-llm-observability-platforms3
gbhackers.com/top-5-ai-gateways-for-enterprise-2026-guide3

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 Confident 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 Confident 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 confident-ai.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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