GTM AI Index
Index Marketing Programmatic › StackAdapt vs Demandbase
Programmatic and display advertising · September 2026 Edition

StackAdapt vs Demandbase

Ten of twelve models named StackAdapt first on the direct prompt; zero named Demandbase. StackAdapt was named by twelve of the twelve models and Demandbase by nine and StackAdapt carries 54 labels and Demandbase 17, so the shares are not directly comparable.

StackAdapt

endorsed leader

Named in nine categories this edition.

Demandbase

criticized challenger

San Francisco, United States, founded 2006. Named in eighteen categories this edition.

First-choice share61%2%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate7%29%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#1#5A position in a field of 14; printed, not drawn.
Labels5417A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, StackAdapt reading right to left. Rank and label count are printed, not drawn.AdRoll ABM was named alongside these two in seven of the twelve direct answers. StackAdapt vs AdRoll · StackAdapt vs The Trade Desk · StackAdapt vs Choozle

Share is the count of first choices across the direct, paraphrase, budget and scale prompts over all twelve models, for a mid-market B2B company; rank is within the category; every quote names the model and the prompt it came from. Both figures come from the programmatic and display advertising page.

By framing

How many of the twelve models made each the first choice, per way of asking, and how many argued against it.
StackAdaptFirst choices, of twelve modelsDemandbase
Direct1001 against StackAdapt · 3 against Demandbase
Paraphrase1012 against Demandbase
Comparative00
Budget-constrained401 against StackAdapt
Scale-constrained40
Negative002 against StackAdapt
Bars are first choices, 0 to 12 each sideModels that argued againstA model can name both, so the two sides of a row do not sum to twelve.

Every model, every framing

The seventy-two answers behind the chart above, one cell each: where StackAdapt and Demandbase stood in it.
ModelDirectParaphraseComparativeBudget-constrainedScale-constrainedNegative
Claude Haiku 4.5
GPT-5.4 mini
Gemini 3.5 Flash
Perplexity Sonar
Grok 4.1 Fast
Mistral Small
DeepSeek V4 Flash
Llama 4 Maverick
Qwen 3.7 Flash
Kimi K2
GLM 4.7 FlashX
MiniMax M2.5
StackAdapt Demandbase first choice named as an alternative argued againstblank: not namedEach cell is one answer, StackAdapt on the left and Demandbase on the right.

The direct prompt

The plain question, one answer per model, grouped by where StackAdapt and Demandbase stood in it.

StackAdapt first, Demandbase an alternative

10 of 12 modelsDemandbase was named in the answer but not as the choice, or not at all.
Claude Haiku 4.5StackAdapt alternatives: AdRoll ABM, Hey Sid, LinkedIn Campaign Manager
Gemini 3.5 FlashStackAdapt alternatives: AdRoll ABM, Choozle, Metadata.io
Perplexity SonarStackAdapt alternatives: Basis, DV360, Demandbase, The Trade Desk
Grok 4.1 FastStackAdapt alternatives: Demandbase, Google Display & Video 360, Hey Sid, LinkedIn Campaign Manager
Mistral SmallStackAdapt alternatives: AdRoll ABM, Hey Sid
DeepSeek V4 FlashStackAdapt alternatives: AdRoll ABM, Demandbase, Hey Sid, LinkedIn Campaign Manager
Llama 4 MaverickStackAdapt
Kimi K2StackAdapt alternatives: AdRoll ABM, Hey Sid
GLM 4.7 FlashXStackAdapt alternatives: AdRoll ABM, Amazon DSP, Google Display & Video 360
MiniMax M2.5StackAdapt alternatives: Google Display & Video 360, Hey Sid, The Trade Desk

Neither was the first choice, one was named

2 of 12 modelsThe answer put something else first and named one of the two as an alternative.
GPT-5.4 miniAdRoll ABM alternatives: 6sense, Demandbase
Qwen 3.7 FlashThe Trade Desk alternatives: Demandbase, Google Display & Video 360, Zeta Global

Bold names in an answer are the products the judge labeled a first choice; a model naming several gives each of them that label. The full answer text for every row is in the record.

By buyer segment

The same question asked on behalf of a different buyer. Each standing is computed within its segment and they are never added together. The figures above are the mid-market standing, which is the one the category orders by.
Small business
StackAdapt leads by thirty-two points.
StackAdapt32%#1 of 12
Demandbase0%#11 of 12
The full small business standing →
Mid-marketThe figures above
StackAdapt leads by fifty-nine points.
StackAdapt61%#1 of 14
Demandbase2%#5 of 14
The full mid-market standing →
Enterprise
The order flips: Demandbase leads at enterprise.
Demandbase17%#2 of 6
StackAdapt9%#3 of 6
The full enterprise standing →

What the models said about StackAdapt

Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Five of five in this category shown.

“Why avoid: If CTV represents over 40% of planned spend, these platforms should be eliminated” GLM 4.7 FlashX · negative prompt · hard negative
“StackAdapt if CTV is a major share of your budget, you need access to major walled gardens” Perplexity Sonar · negative prompt · soft negative
“I'd choose StackAdapt for most companies with a limited budget because it is the most consistently recommended *low-to-mid budget* platform” Perplexity Sonar · budget prompt · first choice
“Highly regarded for its intuitive UI, excellent customer support, absence of heavy minimum spends, and robust AI-driven optimizations” Gemini 3.5 Flash · scale prompt · first choice
“StackAdapt is a standout here. It requires no ad-ops specialist... For B2B / ABM campaigns: Start with StackAdapt.” Qwen 3.7 Flash · scale prompt · first choice

What the models said about Demandbase

Every negative label with a quote, up to three, then the highest-weighted positives, up to three. One of one in this category shown.

“Platforms to Avoid for Mid-Market B2B ... Demandbase/6sense: Enterprise pricing ($50K+/year minimum)” Mistral Small · direct prompt · hard negative
Also compared

Comparisons are drawn for the top eight products in each category, each against each. The output is the models' output; nothing here is a recommendation by the index.