Five of fourteen models named Credo AI first on the direct prompt; zero named Holistic AI. Credo AI was named by fourteen of the fourteen models and Holistic AI by twelve and Credo AI carries 49 labels and Holistic AI 31, 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 AI governance platforms page.
Across every category in the November 2026 Edition, Credo AI and Holistic AI were named in the same answer sixty-seven times, of the 160 answers naming Credo AI and the 73 naming Holistic AI. In those answers Holistic AI took the first choice one time and Credo AI twenty-five.
| Model | Direct | Paraphrase | Comparative | Budget-constrained | Scale-constrained | Negative |
|---|---|---|---|---|---|---|
| Claude Haiku 4.5 | ||||||
| GPT-5.4 mini | ||||||
| Gemini 3.5 Flash | ||||||
| Perplexity Sonar | ||||||
| Grok 4.20 | ||||||
| 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 |
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.
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of eight in this category shown.
“$25K–$45K+/year | Enterprise-focused” DeepSeek V4 Flash · budget prompt · hard negative
“Most traditional GRC ... platforms—like Credo AI, OneTrust, Vanta, Drata, or Holistic AI—are designed more for documentation and reporting, which may not be sufficient” Mistral Small · paraphrase prompt · soft negative
“Tools such as Credo AI, OneTrust, and Holistic AI can be strong for inventories, assessments, policy mapping, and audit evidence, but may not stop unsafe prompts” Perplexity Sonar · negative prompt · soft negative
“Credo AI is widely considered a pioneer in the dedicated AI governance space. Unlike general GRC tools that added AI features later, Credo was built specifically for AI.” Qwen 3.7 Flash · comparative prompt · first choice
“Credo AI pioneered the AI governance category... Best for: Organizations needing a governance-first platform with mature policy automation and audit-ready evidence.” DeepSeek V4 Flash · comparative prompt · first choice
“Credo AI is the most-deployed control plane, a Forrester Wave Leader and Gartner Visionary, making it the best AI governance platform for most enterprises.” Claude Haiku 4.5 · comparative prompt · first choice
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of eight in this category shown.
“Enterprise-only platforms (OneTrust, Holistic AI, ModelOp) – typically $30K–$150K+/year” MiniMax M2.5 · budget prompt · hard negative
“capabilities or compliance claims have not been independently verified; this is explicitly flagged for Credo AI, Holistic AI, ServiceNow, and others” Perplexity Sonar · negative prompt · soft negative
“Both have high negative rates (17% and 16% respectively), which may reflect issues with functionality, support, or integration.” Mistral Small · negative prompt · soft negative
“Purpose-built AI Governance | Credo AI, Trustible, Enzai, Holistic AI | Usually the best fit.” Kimi K2 · scale prompt · first choice
“Holistic AI provides bias auditing, risk management, and compliance in one platform with strong technical depth without enterprise complexity” Claude Haiku 4.5 · direct prompt · alternative
“Offers deeper automated technical testing (bias, hallucinations, toxicity) than most competitors while still covering policy/compliance.” DeepSeek V4 Flash · comparative prompt · alternative
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.