Five of fourteen models named Azure Machine Learning first on the direct prompt; six named Databricks Mosaic AI. Both were named by all fourteen models and Azure Machine Learning carries 49 labels and Databricks Mosaic AI 44, 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 Databricks Mosaic AI were named in the same answer 104 times, of the 159 answers naming Azure Machine Learning and the 136 naming Databricks Mosaic AI. In those answers Databricks Mosaic AI took the first choice twenty-six times and Azure Machine Learning twelve.
| 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 |
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 4.1 Fast · 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.
“Databricks (full platform) | Can be expensive unless you already need the lakehouse architecture” Kimi K2 · paraphrase prompt · hard negative
“Nearly all major platforms (Databricks, SageMaker, Vertex AI, SAS) were flagged by users as having steep learning curves” DeepSeek V4 Flash · negative prompt · soft negative
“Steep learning curve - Confusing for newcomers... High cost - Particularly expensive for large data projects” GLM 4.7 FlashX · negative prompt · soft negative
“Databricks (Mosaic AI) and Google Vertex AI are top choices due to their balance of power, ease of use, and integration with popular cloud ecosystems.” Mistral Small · direct prompt · first choice
“my default pick would be Databricks if you have moderate complexity and want fewer moving parts” Perplexity Sonar · paraphrase prompt · first choice
“I'd suggest starting with Databricks or DataRobot as they balance power with usability” MiniMax M2.5 · direct 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.