Five of fourteen models named Azure Machine Learning first on the direct prompt; one named Amazon SageMaker. Both were named by all fourteen models and Azure Machine Learning carries 49 labels and Amazon SageMaker 51, 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 ML platforms page.
Across every category in the October 2026 Edition, Azure Machine Learning and Amazon SageMaker were named in the same answer 136 times, of the 159 answers naming Azure Machine Learning and the 172 naming Amazon SageMaker. In those answers Amazon SageMaker took the first choice sixteen times and Azure Machine Learning fourteen.
| 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.
“models suggesting caution or avoidance unless you have the necessary expertise and budget” Mistral Small · negative prompt · hard negative
“challenges such as data privacy, vendor lock-in, and cost unpredictability remain persistent concerns” Muse Glimmer 30B · negative prompt · soft negative
“Enterprise Cloud Platforms for Small Teams/Startups (e.g., SageMaker, Vertex AI, Azure ML)” Grok 4.20 · negative prompt · soft negative
“Small team, tight budget, general ML: Azure Machine Learning or SageMaker AI are solid picks.” GPT-5.4 mini · budget prompt · first choice
“If you want a default pick and you're already Microsoft-centric, I'd choose Azure Machine Learning.” GPT-6 Luna · paraphrase prompt · first choice
“Azure Machine Learning and Google Vertex AI are the most broadly suitable platform choices” Perplexity Sonar · direct 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.
“Some AI models recommend avoiding this platform if you are a small team, a startup, or just starting out.” Llama 4 Maverick · negative prompt · hard negative
“models explicitly advising to avoid it unless you have significant resources or a large team” Mistral Small · negative prompt · hard negative
“Avoid enterprise-scale platforms like SageMaker, Dataiku, or DataRobot — they're overkill” DeepSeek V4 Flash · negative prompt · hard negative
“AWS SageMaker and Azure Machine Learning are the most practical managed choices for companies that are already standardized on one cloud.” Muse Glimmer 30B · paraphrase prompt · first choice
“Market Share: ~34% (leader) ... Primary Strength: Broadest ML feature set and deepest AWS integration” Kimi K2 · comparative prompt · first choice
“I'd usually recommend Amazon SageMaker or Google Vertex AI as the best default choices” GPT-5.4 mini · paraphrase 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.