ML platforms · October 2026 Edition
Amazon SageMaker vs DataRobot
One of fourteen models named Amazon SageMaker first on the direct prompt; two named DataRobot. Amazon SageMaker was named by fourteen of the fourteen models and DataRobot by nine and Amazon SageMaker carries 51 labels and DataRobot 19, so the shares are not directly comparable.
Named in two categories this edition.
Named in two categories this edition.
First-choice share11%4%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate25%11%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#3#7A position in a field of 13; printed, not drawn.
Labels5119A 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 ten of the fourteen direct answers. Azure Machine Learning vs Amazon SageMaker · Azure Machine Learning vs DataRobot · Databricks Mosaic AI 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 modelsDataRobot
Direct12
Paraphrase402 against Amazon SageMaker
Comparative20
Budget-constrained103 against Amazon SageMaker
Scale-constrained00
Negative008 against Amazon SageMaker · 2 against DataRobot
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.
Across every category in the October 2026 Edition, Amazon SageMaker and DataRobot were named in the same answer sixty-one times, of the 172 answers naming Amazon SageMaker and the 74 naming DataRobot. In those answers DataRobot took the first choice four times and Amazon SageMaker eight.
Every model, every framing
The eighty-four answers behind the chart above, one cell each: where Amazon SageMaker and DataRobot 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 4.1 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 | | | |
Amazon SageMaker DataRobot first choice named as an alternative argued againstblank: not namedEach cell is one answer, Amazon SageMaker on the left and DataRobot on the right.The direct prompt
The plain question, one answer per model, grouped by where Amazon SageMaker and DataRobot stood in it.
Amazon SageMaker first, DataRobot not the choice
1 of 14 modelsDataRobot was named in the answer but not as the choice, or not at all.Claude Haiku 4.5Amazon SageMaker, Databricks Mosaic AI alternatives: Azure Machine Learning
DataRobot first, Amazon SageMaker an alternative
2 of 14 modelsAmazon SageMaker was named in the answer but not as the choice, or not at all.Qwen 3.7 FlashDataRobot alternatives: Azure Machine Learning, Databricks Mosaic AI, HubSpot Spot Intelligence, Salesforce Einstein, Snowflake Cortex AI
MiniMax M2.5DataRobot, Databricks Mosaic AI alternatives: Amazon SageMaker, Google Vertex AI
Neither was the first choice, one was named
7 of 14 modelsThe answer put something else first and named one of the two as an alternative.GPT-5.4 miniDatabricks Mosaic AI alternatives: Amazon SageMaker, Google Vertex AI
Perplexity SonarAzure Machine Learning, Google Vertex AI alternatives: Amazon Forecast, DataRobot
Grok 4.1 FastAzure Machine Learning alternatives: Amazon SageMaker, DataRobot, Databricks Mosaic AI, Google Vertex AI, H2O.ai
Mistral SmallDatabricks Mosaic AI, Google Vertex AI alternatives: Amazon SageMaker
DeepSeek V4 FlashAzure Machine Learning alternatives: Amazon SageMaker, DataRobot, Databricks Mosaic AI, Dataiku, Google Vertex AI
Kimi K2Azure Machine Learning, Databricks Mosaic AI alternatives: Amazon SageMaker, Google Vertex AI
Muse Glimmer 30BAzure Machine Learning alternatives: Amazon SageMaker, Databricks Mosaic AI
Neither was named
4 of 14 modelsThe answer made no first choice from these two in this category.Gemini 3.5 FlashPecan AI alternatives: Akkio, Azure Machine Learning, Dataiku, Google BigQuery ML, Snowflake Cortex AI
Llama 4 Maverickno first choice
GLM 4.7 FlashXno first choice
GPT-6 LunaDatabricks Mosaic AI alternatives: Azure Machine Learning, Google Vertex AI, SageMaker AI, 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 six points.
Amazon SageMaker8%#3 of 14DataRobot2%#12 of 14Mid-marketThe figures above
Amazon SageMaker leads by seven points.
Amazon SageMaker11%#3 of 13DataRobot4%#7 of 13Enterprise
Amazon SageMaker leads by seven points.
Amazon SageMaker13%#2 of 10DataRobot7%#6 of 10What 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.
“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
What the models said about DataRobot
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Five of six in this category shown.
“Avoid enterprise-scale platforms like SageMaker, Dataiku, or DataRobot — they're overkill” DeepSeek V4 Flash · negative prompt · hard negative
“Tools like DataRobot, Alteryx, or RapidMiner promise to make ML easy... they can be problematic for dedicated data science teams.” Qwen 3.7 Flash · negative prompt · soft negative
“I'd suggest starting with Databricks or DataRobot as they balance power with usability” MiniMax M2.5 · direct prompt · first choice
“The Best Overall for Speed & Ease of Use: DataRobot” Qwen 3.7 Flash · direct prompt · first choice
“DataRobot is the best fit if you want the fastest path to usable models with less ML engineering overhead” Perplexity Sonar · direct prompt · alternative
Also compared
Azure Machine Learning vs Amazon SageMakerAzure Machine Learning vs DataRobotDatabricks Mosaic AI vs Amazon SageMakerDatabricks Mosaic AI vs DataRobotAmazon SageMaker vs Google Vertex AIAmazon SageMaker vs Google ColabAmazon SageMaker vs MLflowAmazon SageMaker vs TensorFlowGoogle Vertex AI vs DataRobotGoogle Colab vs DataRobotMLflow vs DataRobotDataRobot 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.