One of fourteen models named Amazon SageMaker first on the direct prompt; zero named MLflow. Amazon SageMaker was named by fourteen of the fourteen models and MLflow by thirteen and Amazon SageMaker carries 67 labels and MLflow 23, so the shares are not directly comparable.
Named in two categories this edition.
Named in three 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, Amazon SageMaker and MLflow were named in the same answer sixty-one times, of the 198 answers naming Amazon SageMaker and the 73 naming MLflow. In those answers MLflow took the first choice seven times and Amazon SageMaker seven.
| 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
“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
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Five of six in this category shown.
“Free but requires heavy self-management—avoid unless you have strong engineering resources.” Grok 4.20 · direct prompt · soft negative
“great as a component, but it's not usually enough by itself” GPT-5.4 mini · paraphrase prompt · soft negative
“For a budget-conscious company, I'd start with open-source MLflow and run it on the cloud or servers you already use.” GPT-6 Luna · budget prompt · first choice
“We recommend MLflow for teams wanting a free, Apache 2.0 tracking, registry, and serving backbone with no lock-in” Muse Glimmer 30B · paraphrase prompt · first choice
“Best Overall Value: Open-Source Lifecycle Stack ... Experiment Tracking & Model Registry: MLflow” Gemini 3.5 Flash · budget 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.