ML platforms · November 2026 Edition
Amazon SageMaker vs PyTorch
One of fourteen models named Amazon SageMaker first on the direct prompt; zero named PyTorch. Amazon SageMaker was named by fourteen of the fourteen models and PyTorch by eight and Amazon SageMaker carries 67 labels and PyTorch 10, so the shares are not directly comparable.
Named in two categories this edition.
Named in one category this edition.
First-choice share7%3%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate18%10%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#4#7A position in a field of 15; printed, not drawn.
Labels6710A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, Amazon SageMaker reading right to left. Rank and label count are printed, not drawn.Databricks Mosaic AI was named alongside these two in twelve of the fourteen direct answers. Databricks Mosaic AI vs Amazon SageMaker · Databricks Mosaic AI vs PyTorch · Azure Machine Learning vs Amazon SageMaker
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.
By framing
How many of the fourteen models made each the first choice, per way of asking, and how many argued against it.
Amazon SageMakerFirst choices, of fourteen modelsPyTorch
Direct10
Paraphrase20
Comparative40
Budget-constrained023 against Amazon SageMaker
Scale-constrained101 against PyTorch
Negative009 against Amazon SageMaker
Bars are first choices, 0 to 14 each sideModels that argued againstA model can name both, so the two sides of a row do not sum to fourteen.
Every model, every framing
The eighty-four answers behind the chart above, one cell each: where Amazon SageMaker and PyTorch stood in it.
| 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 | | | | |
Amazon SageMaker PyTorch first choice named as an alternative argued againstblank: not namedEach cell is one answer, Amazon SageMaker on the left and PyTorch on the right.The direct prompt
The plain question, one answer per model, grouped by where Amazon SageMaker and PyTorch stood in it.
Amazon SageMaker first, PyTorch not the choice
1 of 14 modelsPyTorch was named in the answer but not as the choice, or not at all.GLM 4.7 FlashXAmazon SageMaker, Azure Machine Learning alternatives: Databricks Mosaic AI, Google Vertex AI
Neither was the first choice, one was named
10 of 14 modelsThe answer put something else first and named one of the two as an alternative.Claude Haiku 4.5no first choice alternatives: Amazon SageMaker, Azure Machine Learning, Clearly.ai, DataRobot, Databricks Mosaic AI, Google Vertex AI, H2O AutoML
GPT-5.4 miniDatabricks Mosaic AI alternatives: Amazon SageMaker, Snowflake Cortex AI
Gemini 3.5 FlashDataiku alternatives: Amazon SageMaker, DataRobot, Databricks Mosaic AI, GCP Vertex AI, Google BigQuery ML, Snowflake Cortex AI
Perplexity SonarAzure Machine Learning alternatives: Amazon SageMaker, Databricks Mosaic AI, Dataiku
Grok 4.20Dataiku alternatives: Amazon SageMaker, Azure Machine Learning, Databricks Mosaic AI, Google Vertex AI
DeepSeek V4 FlashAzure Machine Learning alternatives: Amazon SageMaker, DataRobot, Databricks Mosaic AI, Snowflake Cortex AI
Qwen 3.7 FlashDataRobot alternatives: Amazon SageMaker, Azure Machine Learning, Databricks Mosaic AI
Kimi K2Azure Machine Learning, Dataiku alternatives: Amazon SageMaker
MiniMax M2.5Databricks Mosaic AI, Snowflake Cortex AI alternatives: Amazon SageMaker, Azure Machine Learning, Dataiku, Domino Data Lab, Google Vertex AI
Muse Glimmer 30BAzure Machine Learning, Dataiku alternatives: Amazon SageMaker, Databricks Mosaic AI, Google Vertex AI, Snowflake Cortex AI
Neither was named
3 of 14 modelsThe answer made no first choice from these two in this category.Mistral SmallAzure Machine Learning, Snowflake Cortex AI alternatives: DataRobot, Databricks Mosaic AI, Google Vertex AI
Llama 4 MaverickAzure Machine Learning alternatives: DataRobot, Google Vertex AI, IBM watsonx.ai, Snowflake Cortex AI
GPT-6 LunaDatabricks Mosaic AI alternatives: Azure Machine Learning, Gemini Enterprise Agent Platform, Snowflake Cortex AI
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.
By buyer segment
The same question asked on behalf of a different buyer. Each standing is computed within its segment and they are never added together. The figures above are the mid-market standing, which is the one the category orders by.
Small business
Amazon SageMaker leads by nine points.
Amazon SageMaker9%#5 of 15PyTorch0%#– of 15Mid-marketThe figures above
Amazon SageMaker leads by three points.
Amazon SageMaker7%#4 of 15PyTorch3%#7 of 15Enterprise
Amazon SageMaker leads by eight points.
Amazon SageMaker8%#3 of 11PyTorch0%#– of 11What the models said about Amazon SageMaker
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
What the models said about PyTorch
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Four of five in this category shown.
“Open-source frameworks like TensorFlow, PyTorch, and scikit-learn cost nothing to license... but it breaks down the moment you need organization-wide adoption or regulatory auditability.” Muse Glimmer 30B · scale prompt · soft negative
“Start with open-source frameworks (TensorFlow/PyTorch) if your team has the technical expertise” Claude Haiku 4.5 · budget prompt · first choice
“If you have zero budget, go with PyTorch or TensorFlow on your own hardware.” Kimi K2 · budget prompt · first choice
“tools like PyTorch + MLflow + Docker + a basic cloud VM can be the cheapest scalable option” GPT-5.4 mini · budget prompt · alternative
Also compared
Databricks Mosaic AI vs Amazon SageMakerDatabricks Mosaic AI vs PyTorchAzure Machine Learning vs Amazon SageMakerAzure Machine Learning vs PyTorchDataiku vs Amazon SageMakerDataiku vs PyTorchAmazon SageMaker vs MLflowAmazon SageMaker vs Snowflake Cortex AIAmazon SageMaker vs TensorFlowMLflow vs PyTorchSnowflake Cortex AI vs PyTorchPyTorch vs TensorFlowAll twenty-eight head-to-head pages and the full ML platforms standing
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.