ML platforms · October 2026 Edition
Google Vertex AI vs DataRobot
Two of fourteen models named Google Vertex AI first on the direct prompt; two named DataRobot. Google Vertex AI was named by fourteen of the fourteen models and DataRobot by nine and Google Vertex AI carries 57 labels and DataRobot 19, so the shares are not directly comparable.
Named in three categories this edition.
Named in two categories this edition.
First-choice share9%4%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate19%11%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#4#7A position in a field of 13; printed, not drawn.
Labels5719A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, Google Vertex AI 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 Google Vertex AI · Azure Machine Learning vs DataRobot · Databricks Mosaic AI vs Google Vertex AI
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.
Google Vertex AIFirst choices, of fourteen modelsDataRobot
Direct22
Paraphrase20
Comparative001 against Google Vertex AI
Budget-constrained101 against Google Vertex AI
Scale-constrained00
Negative009 against Google Vertex AI · 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, Google Vertex AI and DataRobot were named in the same answer fifty-seven times, of the 173 answers naming Google Vertex AI and the 74 naming DataRobot. In those answers DataRobot took the first choice three times and Google Vertex AI six.
Every model, every framing
The eighty-four answers behind the chart above, one cell each: where Google Vertex AI 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 | | |
Google Vertex AI DataRobot first choice named as an alternative argued againstblank: not namedEach cell is one answer, Google Vertex AI on the left and DataRobot on the right.The direct prompt
The plain question, one answer per model, grouped by where Google Vertex AI and DataRobot stood in it.
Google Vertex AI first, DataRobot an alternative
2 of 14 modelsDataRobot was named in the answer but not as the choice, or not at all.Perplexity SonarAzure Machine Learning, Google Vertex AI alternatives: Amazon Forecast, DataRobot
Mistral SmallDatabricks Mosaic AI, Google Vertex AI alternatives: Amazon SageMaker
DataRobot first, Google Vertex AI an alternative
2 of 14 modelsGoogle Vertex AI 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
5 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
Grok 4.1 FastAzure Machine Learning alternatives: Amazon SageMaker, DataRobot, Databricks Mosaic AI, Google Vertex AI, H2O.ai
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
GPT-6 LunaDatabricks Mosaic AI alternatives: Azure Machine Learning, Google Vertex AI, SageMaker AI, Snowflake Cortex AI
Neither was named
5 of 14 modelsThe answer made no first choice from these two in this category.Claude Haiku 4.5Amazon SageMaker, Databricks Mosaic AI alternatives: Azure Machine Learning
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
Muse Glimmer 30BAzure Machine Learning alternatives: Amazon SageMaker, Databricks Mosaic 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
Google Vertex AI leads by seventeen points.
Google Vertex AI19%#1 of 14DataRobot2%#12 of 14Mid-marketThe figures above
Google Vertex AI leads by five points.
Google Vertex AI9%#4 of 13DataRobot4%#7 of 13Enterprise
The order flips: DataRobot leads at enterprise.
DataRobot7%#6 of 10Google Vertex AI4%#7 of 10What the models said about Google Vertex AI
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of eight in this category shown.
“it is often recommended to avoid for small teams or startups due to complexity and cost concerns” Mistral Small · negative prompt · hard negative
“Some AI models advise against using this service for enterprise tasks until stability improves.” Llama 4 Maverick · negative prompt · hard negative
“be cautious if you are not already on Google Cloud, have a limited budget, or need a small-scale/simple setup” Perplexity Sonar · negative prompt · soft negative
“the most recommended MLOps platforms for training and deploying models are Vertex AI and Amazon SageMaker” Mistral Small · paraphrase prompt · first choice
“Azure Machine Learning and Google Vertex AI are the most broadly suitable platform choices” Perplexity Sonar · direct 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 Google Vertex AIAzure Machine Learning vs DataRobotDatabricks Mosaic AI vs Google Vertex AIDatabricks Mosaic AI vs DataRobotAmazon SageMaker vs Google Vertex AIAmazon SageMaker vs DataRobotGoogle Vertex AI vs Google ColabGoogle Vertex AI vs MLflowGoogle Vertex AI vs TensorFlowGoogle 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.