StackAdapt vs Amazon DSP
Eleven of fourteen models named StackAdapt first on the direct prompt; zero named Amazon DSP. StackAdapt was named by fourteen of the fourteen models and Amazon DSP by thirteen and StackAdapt carries 60 labels and Amazon DSP 32, so the shares are not directly comparable.
Named in eight categories this edition.
By Amazon, Seattle, United States, founded 1994. Named in six categories this edition.
Share is the count of first choices across the direct, paraphrase, budget and scale prompts over all fourteen 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
StackAdaptFirst choices, of fourteen modelsAmazon DSP
Across every category in the October 2026 Edition, StackAdapt and Amazon DSP were named in the same answer eighty-four times, of the 388 answers naming StackAdapt and the 159 naming Amazon DSP. In those answers Amazon DSP took the first choice two times and StackAdapt twenty.
Every model, every framing
| Model | Direct | Paraphrase | Comparative | Budget-constrained | Scale-constrained | Negative |
|---|---|---|---|---|---|---|
| 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 | | | | | ||
| GPT-6 Luna | | | | | ||
| Muse Glimmer 30B | | | | |
StackAdapt Amazon DSP first choice named as an alternative argued againstblank: not namedEach cell is one answer, StackAdapt on the left and Amazon DSP on the right.The direct prompt
StackAdapt first, Amazon DSP not the choice
11 of 14 modelsAmazon DSP was named in the answer but not as the choice, or not at all.Neither was named
3 of 14 modelsThe answer made no first choice from these two in this category.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
StackAdapt43%#1 of 15Amazon DSP0%#15 of 15StackAdapt48%#1 of 13Amazon DSP3%#5 of 13StackAdapt6%#4 of 10Amazon DSP4%#5 of 10What the models said about StackAdapt
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Three of three in this category shown.
“Good self-serve option too, but it's generally positioned more as an advanced multichannel DSP with custom usage-based pricing rather than an obviously low-entry-budget tool.” GPT-5.4 mini · budget prompt · soft negative
“often considered by smaller teams, but I'd want to verify your exact use case before recommending it over The Trade Desk or Demandbase” GPT-5.4 mini · direct prompt · soft negative
“strong for smaller agencies, but the budget guidance in the results suggests it is better once spend is a bit higher” Perplexity Sonar · budget prompt · soft negative
What the models said about Amazon DSP
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Four of four in this category shown.
“Amazon DSP ($35K+) have high thresholds, complex contracts, and are geared toward large-scale operations” Grok 4.1 Fast · budget prompt · hard negative
“$35K/month minimum, retail-focused, limited B2B utility” Kimi K2 · paraphrase prompt · hard negative
“generally considered to offer better safety controls, more transparency, and stronger fraud prevention mechanisms” MiniMax M2.5 · negative prompt · first choice
“Prioritize platforms like Google DV360, The Trade Desk, MediaMath, or Amazon DSP, but test via demos or pilots.” Grok 4.1 Fast · scale prompt · first choice
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.