Three of fourteen models named Databricks Mosaic AI first on the direct prompt; one named Amazon SageMaker. Both were named by all fourteen models and Databricks Mosaic AI carries 53 labels and Amazon SageMaker 67, so the shares are not directly comparable.
Named in one category 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 November 2026 Edition, Databricks Mosaic AI and Amazon SageMaker were named in the same answer 137 times, of the 151 answers naming Databricks Mosaic AI and the 198 naming Amazon SageMaker. In those answers Amazon SageMaker took the first choice seventeen times and Databricks Mosaic AI thirty-eight.
| 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.
“Avoid heavy enterprise tools like Databricks or full SageMaker unless you have specific needs” Grok 4.20 · budget prompt · hard negative
“Why to be Cautious: These platforms often have steep learning curves and high entry costs.” Qwen 3.7 Flash · negative prompt · soft negative
“without the complexity and cost of full enterprise solutions like Databricks or Palantir” Kimi K2 · direct prompt · soft negative
“Leaders: Databricks (Unity Catalog, MLflow)... the "big three" cloud-native platforms (AWS, Azure, GCP) or Databricks are often the safest bets” MiniMax M2.5 · scale prompt · first choice
“choose Databricks MLflow/Mosaic AI or a pure MLflow stack if you prioritize vendor neutrality, data-lake integration and lower long-term cost” Muse Glimmer 30B · paraphrase prompt · first choice
“Databricks (MLflow / Mosaic AI) - Best for organizations unifying data engineering and machine learning on a single lakehouse.” Llama 4 Maverick · paraphrase 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.
“Avoid heavy enterprise tools like Databricks or full SageMaker unless you have specific needs” Grok 4.20 · budget prompt · hard negative
“SageMaker, Vertex AI, and Azure ML can simplify operations, but their proprietary APIs, registries, pipelines, and deployment workflows can create vendor lock-in.” Perplexity Sonar · negative prompt · soft negative
“Heavily Integrated Cloud Platforms (e.g., AWS SageMaker, Google Vertex AI, Microsoft Azure ML): Why cautious? Strong vendor lock-in” Grok 4.20 · negative prompt · soft negative
“Start with SageMaker AI, Gemini Enterprise Agent Platform, or Azure Machine Learning if you're committed to that cloud.” GPT-6 Luna · comparative prompt · first choice
“Azure Machine Learning or AWS SageMaker provide the best balance of features, scalability, and cost-effectiveness” GLM 4.7 FlashX · direct prompt · first choice
“recommend a managed cloud-native platform—usually AWS SageMaker AI, Azure Machine Learning, or Google Vertex AI” Perplexity Sonar · 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.