ML platforms · November 2026 Edition
Dataiku vs TensorFlow
Four of fourteen models named Dataiku first on the direct prompt; zero named TensorFlow. Dataiku was named by eleven of the fourteen models and TensorFlow by six and Dataiku carries 23 labels and TensorFlow 10, so the shares are not directly comparable.
Named in two categories this edition.
Named in one category this edition.
First-choice share9%3%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate0%10%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#3#8A position in a field of 15; printed, not drawn.
Labels2310A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, Dataiku 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 Dataiku · Databricks Mosaic AI vs TensorFlow · Azure Machine Learning vs Dataiku
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.
DataikuFirst choices, of fourteen modelsTensorFlow
Direct40
Paraphrase00
Comparative00
Budget-constrained12
Scale-constrained001 against TensorFlow
Negative00
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 Dataiku and TensorFlow 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 | | | |
Dataiku TensorFlow first choice named as an alternative argued againstblank: not namedEach cell is one answer, Dataiku on the left and TensorFlow on the right.The direct prompt
The plain question, one answer per model, grouped by where Dataiku and TensorFlow stood in it.
Dataiku first, TensorFlow not the choice
4 of 14 modelsTensorFlow was named in the answer but not as the choice, or not at all.Gemini 3.5 FlashDataiku alternatives: Amazon SageMaker, DataRobot, Databricks Mosaic AI, GCP Vertex AI, Google BigQuery ML, Snowflake Cortex AI
Grok 4.20Dataiku alternatives: Amazon SageMaker, Azure Machine Learning, Databricks Mosaic AI, Google Vertex AI
Kimi K2Azure Machine Learning, Dataiku alternatives: Amazon SageMaker
Muse Glimmer 30BAzure Machine Learning, Dataiku alternatives: Amazon SageMaker, Databricks Mosaic AI, Google Vertex AI, Snowflake Cortex AI
Neither was the first choice, one was named
2 of 14 modelsThe answer put something else first and named one of the two as an alternative.Perplexity SonarAzure Machine Learning alternatives: Amazon SageMaker, Databricks Mosaic AI, Dataiku
MiniMax M2.5Databricks Mosaic AI, Snowflake Cortex AI alternatives: Amazon SageMaker, Azure Machine Learning, Dataiku, Domino Data Lab, Google Vertex AI
Neither was named
8 of 14 modelsThe answer made no first choice from these two in this category.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
Mistral SmallAzure Machine Learning, Snowflake Cortex AI alternatives: DataRobot, Databricks Mosaic AI, Google Vertex AI
DeepSeek V4 FlashAzure Machine Learning alternatives: Amazon SageMaker, DataRobot, Databricks Mosaic AI, Snowflake Cortex AI
Llama 4 MaverickAzure Machine Learning alternatives: DataRobot, Google Vertex AI, IBM watsonx.ai, Snowflake Cortex AI
Qwen 3.7 FlashDataRobot alternatives: Amazon SageMaker, Azure Machine Learning, Databricks Mosaic AI
GLM 4.7 FlashXAmazon SageMaker, Azure Machine Learning alternatives: Databricks Mosaic AI, Google Vertex 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
Level: the same share of first choices.
Dataiku2%#11 of 15TensorFlow2%#– of 15Mid-marketThe figures above
Enterprise
What the models said about Dataiku
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Three of four in this category shown.
“Best for limited data-science headcount + governance: Dataiku. End-to-end, no/low/full-code, strong governance/monitoring and vendor-agnostic.” Muse Glimmer 30B · direct prompt · first choice
“starting with KNIME or Dataiku's free tier allows you to prove value without spending money” Qwen 3.7 Flash · budget prompt · first choice
“Dataiku and Azure Machine Learning are frequently recommended as the sweet spot” Kimi K2 · direct prompt · first choice
What the models said about TensorFlow
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
“use TensorFlow / PyTorch via Anaconda environments for code-based work” Muse Glimmer 30B · budget prompt · alternative
Also compared
Databricks Mosaic AI vs DataikuDatabricks Mosaic AI vs TensorFlowAzure Machine Learning vs DataikuAzure Machine Learning vs TensorFlowDataiku vs Amazon SageMakerDataiku vs MLflowDataiku vs Snowflake Cortex AIDataiku vs PyTorchAmazon SageMaker vs TensorFlowMLflow vs TensorFlowSnowflake Cortex AI vs TensorFlowPyTorch 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.