ML platforms · September 2026 Edition
Databricks vs Amazon SageMaker
Two of twelve models named Databricks first on the direct prompt; one named Amazon SageMaker. Both were named by all twelve models and Databricks carries 30 labels and Amazon SageMaker 49, so the shares are not directly comparable.
Named in three categories this edition.
Named in one category this edition.
First-choice share12%10%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate10%29%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#2#3A position in a field of 12; printed, not drawn.
Labels3049A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, Databricks reading right to left. Rank and label count are printed, not drawn.Azure Machine Learning was named alongside these two in ten of the twelve direct answers. Azure Machine Learning vs Databricks · Azure Machine Learning vs Amazon SageMaker
Share is the count of first choices across the direct, paraphrase, budget and scale prompts over all twelve 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 twelve models made each the first choice, per way of asking, and how many argued against it.
DatabricksFirst choices, of twelve modelsAmazon SageMaker
Direct212 against Amazon SageMaker
Paraphrase442 against Amazon SageMaker
Comparative01
Budget-constrained001 against Databricks · 2 against Amazon SageMaker
Scale-constrained00
Negative002 against Databricks · 8 against Amazon SageMaker
Bars are first choices, 0 to 12 each sideModels that argued againstA model can name both, so the two sides of a row do not sum to twelve.
The direct prompt
The plain question, one answer per model, grouped by where Databricks and Amazon SageMaker stood in it.
Databricks first, Amazon SageMaker an alternative
2 of 12 modelsAmazon SageMaker was named in the answer but not as the choice, or not at all.GPT-5.4 miniDatabricks alternatives: Amazon SageMaker, Azure Machine Learning
MiniMax M2.5Azure Machine Learning, Databricks alternatives: Google Vertex AI, Snowflake Cortex AI
Amazon SageMaker first, Databricks not the choice
1 of 12 modelsDatabricks was named in the answer but not as the choice, or not at all.DeepSeek V4 FlashAmazon SageMaker, Azure Machine Learning, Google Vertex AI alternatives: DataRobot, Dataiku, H2O.ai, KNIME
Neither was the first choice, one was named
4 of 12 modelsThe answer put something else first and named one of the two as an alternative.Gemini 3.5 FlashDataiku alternatives: Azure Machine Learning, DataRobot, Databricks, Pecan AI, Snowflake
Mistral SmallAzure Machine Learning alternatives: Amazon SageMaker, Databricks Mosaic AI, Dataiku, Google Vertex AI
Qwen 3.7 FlashAzure Machine Learning alternatives: Amazon SageMaker, Google Vertex AI
GLM 4.7 FlashXDataiku alternatives: Azure Machine Learning, DataRobot, Databricks
Neither was named
5 of 12 modelsThe answer made no first choice from these two in this category.Claude Haiku 4.5no first choice
Perplexity SonarAzure Machine Learning alternatives: Databricks Mosaic AI, Dataiku, Snowflake Cortex AI
Grok 4.1 FastAzure Machine Learning alternatives: DataRobot, Databricks Mosaic AI, Dataiku
Llama 4 Maverickno first choice
Kimi K2Dataiku alternatives: Azure Machine Learning, 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
Databricks leads by two points.
Databricks4%#5 of 14Amazon SageMaker2%#9 of 14Mid-marketThe figures above
Databricks leads by two points.
Databricks12%#2 of 12Amazon SageMaker10%#3 of 12Enterprise
Databricks leads by seven points.
Databricks26%#1 of 9Amazon SageMaker19%#2 of 9What the models said about Databricks
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Four of four in this category shown.
“Platforms with aggressive model deprecation policies (like some Azure Databricks and OpenAI services) can disrupt your operations” Kimi K2 · negative prompt · soft negative
“I'd recommend starting with either Databricks or Azure Machine Learning” MiniMax M2.5 · direct prompt · first choice
“Best overall "sweet spot": Databricks (with MLflow) — my top recommendation” DeepSeek V4 Flash · paraphrase prompt · first choice
“I'd usually recommend Databricks as the default MLOps platform” GPT-5.4 mini · paraphrase prompt · first choice
What 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 these platforms if you are a small team, a startup, or just starting out.” GLM 4.7 FlashX · negative prompt · hard negative
“SageMaker is notoriously complex, has a steep learning curve, and often drains mid-market budgets through hidden configuration costs” Gemini 3.5 Flash · direct prompt · soft negative
“may not be suitable for teams that prioritize open-source flexibility or operate in a multi-cloud environment due to vendor lock-in” Llama 4 Maverick · negative prompt · soft negative
“I'd recommend starting with your existing cloud provider—most often Amazon SageMaker (if on AWS), Google Vertex AI (GCP), or Azure Machine Learning (Azure)” Grok 4.1 Fast · paraphrase prompt · first choice
“I'd usually recommend starting with the managed ML service on whichever cloud you already use (SageMaker, Azure ML, or Vertex AI)” DeepSeek V4 Flash · direct prompt · first choice
“Amazon SageMaker and Databricks are the top choices due to their scalability, integration, and comprehensive feature sets.” Mistral Small · paraphrase prompt · first choice
Also compared
Comparisons are drawn for the top three products in each category. The output is the models' output; nothing here is a recommendation by the index.