DataDome vs HUMAN Security
Five of fourteen models named DataDome first on the direct prompt; zero named HUMAN Security. DataDome was named by fourteen of the fourteen models and HUMAN Security by eight and DataDome carries 49 labels and HUMAN Security 18, so the shares are not directly comparable.
Named in two categories this edition.
Named in two 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 bot management page.
By framing
DataDomeFirst choices, of fourteen modelsHUMAN Security
Across every category in the October 2026 Edition, DataDome and HUMAN Security were named in the same answer fifty-three times, of the 149 answers naming DataDome and the 94 naming HUMAN Security. In those answers HUMAN Security took the first choice zero times and DataDome fifteen.
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 | |
DataDome HUMAN Security first choice named as an alternative argued againstblank: not namedEach cell is one answer, DataDome on the left and HUMAN Security on the right.The direct prompt
DataDome first, HUMAN Security an alternative
5 of 14 modelsHUMAN Security was named in the answer but not as the choice, or not at all.Neither was the first choice, one was named
7 of 14 modelsThe answer put something else first and named one of the two as an alternative.Neither was named
2 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
DataDome6%#3 of 7HUMAN Security0%#– of 7DataDome22%#2 of 9HUMAN Security2%#4 of 9DataDome17%#2 of 8HUMAN Security4%#4 of 8What the models said about DataDome
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of eight in this category shown.
“If you are currently evaluating dedicated bot-management vendors (such as DataDome, HUMAN, Kasada, Akamai, or F5), watch out for these contract and technical warning signs” Gemini 3.5 Flash · negative prompt · soft negative
“DataDome's published Essentials plan starts at $3,830/month, so I'd reserve it for a clear need and budget.” GPT-6 Luna · paraphrase prompt · soft negative
“No free tier (free trial only) | Enterprise‑only pricing (custom quotes) ... can budget >$200/mo” GLM 4.7 FlashX · budget prompt · soft negative
“DataDome | Real-time, multi-surface protection | Sub-2ms latency, <0.01% false positives, strong API/mobile coverage, 24/7 SOC | Can be pricier” DeepSeek V4 Flash · scale prompt · first choice
“If you have the budget (~$45k/year) and want the highest protection with the least amount of internal work, go with DataDome.” Gemini 3.5 Flash · direct prompt · first choice
“DataDome (Leader in Forrester Wave) ... Superior AI-agent trust management, zero-friction UX, and excellent API-level protection.” Gemini 3.5 Flash · comparative prompt · first choice
What the models said about HUMAN Security
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. One of one in this category shown.
“Leader in Forrester Wave Q3 2024 with top scores in strategy, detection models... #1 on G2 for Bot Detection and Mitigation” Grok 4.1 Fast · comparative 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.