IT AI Index
September 2026 Edition · The permanent record of this edition. The unqualified address always carries the latest edition.
Index › Data platform › ML platforms › Enterprise › September 2026 Edition

ML platforms for enterprise buyers

Asked as “machine learning platform”, and as “MLOps platform for training and deploying models”, on behalf of an enterprise B2B company. 42 first choices recorded across the direct, paraphrase, budget and scale prompts, twelve models each.
Standing
Databricks
Contested
26% of first choices, contested.
ShowinginAll 67 categories in the September 2026 Edition →

By buyer segment

The same question asked on behalf of a different buyer. Each standing is computed within its segment; they sit side by side and are never added together.
Small businessGoogle Vertex AI17%ContestedStanding →Mid-marketAzure Machine Learning17%ContestedStanding →EnterpriseDatabricks26%ContestedThis page

01The standing

Share is the count of first choices across the direct, paraphrase, budget and scale prompts, over all twelve models, for an enterprise B2B company. Ordered by share.
ProductFirst-choice shareNegative rateLabelsQuadrant
01Databricks26%10%42accepted challenger
02Amazon SageMaker19%21%56accepted challenger
03Azure Machine Learning12%12%49accepted challenger
04Dataiku7%4%26accepted challenger
05Domino Data Lab7%0%16accepted challenger
06Google Vertex AI5%20%51accepted challenger
07DataRobot5%14%28accepted challenger
08H2O.ai2%17%12accepted challenger
Show the one product at 0%, ordered by negative rate
09MLflow0%20%10accepted challenger
Bars are the share of first choices, 0 to 100Every product with at least 10 labels here. Every product name links to its vendor page.
Head to head
Azure Machine Learning vs DatabricksAzure Machine Learning vs Amazon SageMakerDatabricks vs Amazon SageMaker

Recommended versus criticized

Every product with at least 10 labels here, on both axes. The 30% line names a quadrant, not the verdict above: that one needs more than 40%.

Criticized challengerCriticized default
Negative label rate →
01
02
03
04
05
06
07
08
09
Accepted challengerEndorsed leader
0%First-choice share → · lines at 30% share and 25% negative40%
Key
01Databricks26%
02Amazon SageMaker19%
03Azure Machine Learning12%
04Dataiku7%
05Domino Data Lab7%
06Google Vertex AI5%
07DataRobot5%
08H2O.ai2%
09MLflow0%

02What they warned about

One of twelve models held their first choice under the paraphrase. Claude Haiku 4.5, GPT-5.4 mini, Gemini 3.5 Flash, Perplexity Sonar, Mistral Small, DeepSeek V4 Flash, Llama 4 Maverick, Qwen 3.7 Flash, Kimi K2, GLM 4.7 FlashX and MiniMax M2.5 changed. A high negative share on a product with few labels is a warning. A low share on a product with many labels is salience, not sentiment.
Google Vertex AI
20%
10 of 51 labels negative · 8 of 12 models · 2 hard negative
“**Google Vertex AI** has historically scored significantly lower... **Why avoid it:** It is often cited as being complex and fragmented.” GLM 4.7 FlashX, negative prompt
Amazon SageMaker
21%
12 of 56 labels negative · 8 of 12 models · 1 hard negative
“Platforms to Avoid for Predictable Costs ... **AWS SageMaker** | Pay-per-hour for notebooks, training, inference, storage—costs explode with usage” Kimi K2, budget prompt
Azure Machine Learning
12%
6 of 49 labels negative · 5 of 12 models · 1 hard negative
“Similar usage-based model; compute costs vary dramatically” Kimi K2, budget prompt
Kubeflow
62%
5 of 8 labels negative · 5 of 12 models · 1 hard negative
“**Kubeflow** | **Avoid** for simple use cases | Unnecessary complexity and overhead” Kimi K2, negative prompt

03What they cite

Citations exist only for the models that return a source list: twelve of the twelve in this edition, and all six flagship models on the expanded tier.

Sites the answers cite

62 of 72 answers in this category came back with a source list, from 12 of 12 models: citations where the model returns them, or the search results it consulted. 6 of those lists are Google grounding redirects that name no site and are left out of the counts. 901 links across 317 sites, every framing counted. Ranked by the number of answers carrying the site or page.

checkthat.ai
25 answers · 34 citations · 10 models
dataiku.com
vendor site · Dataiku24 answers · 26 citations · 9 models
g2.com
21 answers · 26 citations · 9 models
learn.g2.com
19 answers · 29 citations · 9 models
artificialintelligencecompanies.com
16 answers · 16 citations · 9 models
truefoundry.com
vendor site · TrueFoundry15 answers · 15 citations · 8 models
aiadvisorypractice.com
14 answers · 20 citations · 7 models
techtarget.com
13 answers · 15 citations · 8 models
azumo.com
11 answers · 12 citations · 6 models
guptadeepak.com
10 answers · 10 citations · 5 models
linkedin.com
10 answers · 10 citations · 6 models
thirstysprout.com
10 answers · 10 citations · 6 models

Pages the answers cite

The ten pages named in the most answers, by full address. A page here is one the models returned with a recommendation, not one the index endorses.

checkthat.ai/answers/what-are-the-best-ai-ml-platforms
20 answers · 9 models
dataiku.com/blog/enterprise-machine-learning-platforms-how-to-choose
18 answers · 8 models
artificialintelligencecompanies.com/best/machine-learning-platforms
16 answers · 9 models
learn.g2.com/best-machine-learning-tools
15 answers · 8 models
learn.g2.com/best-data-science-ml-platforms
14 answers · 8 models
aiadvisorypractice.com/blog/mlops-platform-comparison-enterprise-buyers
10 answers · 7 models
guptadeepak.com/tools/top-10-mlops-platforms-2026
10 answers · 5 models
thirstysprout.com/post/best-machine-learning-platforms
10 answers · 6 models
veritis.com/blog/best-mlops-tools-for-enterprises
10 answers · 8 models
articsledge.com/post/machine-learning-platforms-comparison-azure-ai-vs-aws-…
9 answers · 4 models

04How they answered

Six framings of the same buying question, each sent to every model in a fresh session with search on. One row per model, so a row shows whether it held its answer under rewording, what it named when cost was the constraint, and what it argued against. Computed from the raw judge labels.
ModelDirect“What is the best machine learning platform for an enterprise B2B company?”Paraphrase“Which MLOps platform for training and deploying models would you recommend to a large B2B company with thousands of employees?”Comparative“What are the top enterprise-grade machine learning platforms and how do they differ?”Budget-constrained“What is the best machine learning platform for a large company that needs predictable total cost across thousands of users?”Scale-constrained“We are a 5,000 person company with SSO, SOC 2 and procurement review requirements evaluating a machine learning platform. What should we look at?”Negative“Which machine learning platforms should a large enterprise avoid or be cautious about?”
Claude Haiku 4.5Amazon SageMaker
Six alternativesDatabricks, Domino Data Lab, Google Vertex AI, H2O.ai, IBM watsonx.ai, Snowflake Cortex AI
DatabricksChanged
Three alternativesAmazon SageMaker, Azure Machine Learning, Google Vertex AI
against: Kubeflowno first choiceAmazon SageMaker
Seven alternativesAzure Machine Learning, Databricks, Domino Data Lab, Google Vertex AI, H2O.ai, NVIDIA AI Enterprise, Snowflake Cortex
no first choiceagainst: Amazon SageMaker, Azure Machine Learning, Google Vertex AI
GPT-5.4 miniDatabricks
Three alternativesAmazon SageMaker, Azure Machine Learning, Google Vertex AI
Azure Machine Learning, DatabricksChanged
Two alternativesAmazon SageMaker, Google Vertex AI
no first choiceSnowflake ML
One alternativeDatabricks
no first choicenothing named
Gemini 3.5 FlashGoogle Vertex AI
Six alternativesAmazon SageMaker, Azure Machine Learning, DataRobot, Databricks, Dataiku, IBM watsonx
DatabricksChanged
Six alternativesAmazon SageMaker, Azure Machine Learning, DataRobot, Domino Data Lab, Google Vertex AI, MLflow
Databricks
Five alternativesAmazon SageMaker, Azure Machine Learning, DataRobot, Google Vertex AI, IBM watsonx
Dataiku
Two alternativesDataRobot, Domino Data Lab
against: Amazon SageMaker, Databricks, Google Vertex AIno first choiceagainst: Alteryx, Apache Airflow, DataRobot, H2O Driverless AI, IBM SPSS, Kubeflow, SAS
Perplexity SonarAzure Machine Learning
Five alternativesDatabricks Mosaic AI, Gemini Enterprise Agent Platform, Google Vertex AI, IBM watsonx / Watson Machine Learning, SAS Viya
Databricks Mosaic AIChanged
Seven alternativesAmazon SageMaker, Azure Machine Learning, Dataiku, Domino Data Lab, Google Vertex AI, Kubeflow, MLflow
no first choiceDataiku
Three alternativesAmazon SageMaker, Azure Machine Learning, Google Vertex AI
no first choicenothing named
Grok 4.1 FastDatabricks
Four alternativesAmazon SageMaker, Azure Machine Learning, DataRobot, Google Vertex AI
DatabricksHeld
Three alternativesAmazon SageMaker, Azure Machine Learning, Google Vertex AI
no first choiceDataRobot, Domino Data Lab
Five alternativesAmazon SageMaker, Azure Machine Learning, Databricks, Dataiku, Google Vertex AI
no first choiceagainst: Amazon SageMaker, DataRobot, Google Vertex AI, H2O.ai, Kubeflow, MLflow, RapidMiner, TensorFlow Extended/TFX
Mistral SmallAzure Machine Learning, Databricks
Three alternativesAmazon SageMaker, DataRobot, Google Vertex AI
DatabricksChanged
Three alternativesAmazon SageMaker, Azure Machine Learning, Google Vertex AI
Databricks
Nine alternativesAmazon SageMaker, Azure Machine Learning, DataRobot, Dataiku, Domino Data Lab, Google Vertex AI, H2O.ai, IBM Watson Studio, SAS Viya
Gemini Enterprise Agent Platform, Machine Learning at Scaleagainst: Amazon SageMakerno first choiceagainst: Amazon SageMaker, Azure Machine Learning, Google Vertex AI, HPE Ezmeral
DeepSeek V4 FlashAmazon SageMaker
Five alternativesDatabricks, Dataiku, Google Vertex AI, IBM watsonx, Microsoft Azure ML / Azure AI Foundry
against: DataRobotDatabricksChanged
Three alternativesAmazon SageMaker, Azure Machine Learning, Google Vertex AI
no first choice
Eight alternativesAmazon SageMaker, DataRobot, Databricks, Dataiku, Domino Data Lab, Google Vertex AI, H2O.ai, Microsoft Azure Machine Learning / Azure AI Foundry
Domino Data Lab
Three alternativesAnaconda, DataRobot, Dataiku
against: Amazon SageMaker, Azure Machine Learning, Databricks, Google Vertex AI, H2O.aino first choiceagainst: AWS DeepComposer, AWS DeepLens, Amazon Bedrock, Amazon Lookout for Equipment, Amazon SageMaker, Anthropic, Azure Machine Learning, DataRobot, Dataiku, Google Vertex AI, H2O Driverless AI, OpenAI
Llama 4 Maverickno first choiceDatabricksChanged
Five alternativesAmazon SageMaker, Azure Machine Learning, Dataiku, Google Vertex AI, TrueFoundry
no first choiceDataRobot, Domino Data Lab
Three alternativesAmazon SageMaker, Azure Machine Learning, Databricks
no first choiceagainst: Databricks, MLflow
Qwen 3.7 Flashno first choice
Seven alternativesAmazon SageMaker, Azure Machine Learning, DataRobot, Databricks, Google Vertex AI, Palantir Foundry, Snowflake
DatabricksChanged
Five alternativesAmazon SageMaker, Azure Machine Learning, DataRobot, Domino Data Lab, Google Vertex AI
against: Kubeflowno first choice
Five alternativesAmazon SageMaker, Azure Machine Learning, Databricks, Dataiku, Google Vertex AI
Azure Machine Learning
Three alternativesDatabricks, Hugging Face, Weights & Biases
against: Amazon SageMakerno first choiceagainst: Amazon SageMaker, Azure Machine Learning, Google Vertex AI, Hugging Face, Jupyter Notebook, Kaggle
Kimi K2Amazon SageMaker
Four alternativesDataRobot, Databricks Mosaic AI, Google Vertex AI, Microsoft Azure Machine Learning / AI Foundry
Databricks Mosaic AIChanged
Four alternativesAmazon SageMaker, Azure Machine Learning, Google Vertex AI, MLflow
no first choiceDataiku, H2O.ai
One alternativeAlteryx
against: Amazon SageMaker, Azure Machine Learning, Databricks, Google Vertex AIno first choiceagainst: Amazon SageMaker, ChatGPT, Claude, DagsHub, Google Vertex AI, Kubeflow
GLM 4.7 FlashXDatabricks Mosaic AI
Eight alternativesAmazon SageMaker, Azure Machine Learning, C3 AI, DataRobot, Dataiku, Google Vertex AI, IBM watsonx.ai, Palantir Foundry / Gotham
Amazon SageMakerChanged
Three alternativesAzure Machine Learning, Databricks, IBM Watson Machine Learning
Amazon SageMaker, Databricks
Three alternativesAzure Machine Learning, DataRobot, Google Vertex AI
Amazon SageMaker
Two alternativesAzure Machine Learning, DataRobot
no first choiceagainst: Amazon SageMaker, Flowise, Google Vertex AI, Open WebUI, n8n
MiniMax M2.5Amazon SageMaker, Google Vertex AI
Three alternativesAzure Machine Learning, Databricks, Dataiku
Amazon SageMaker, Azure Machine LearningChanged
Three alternativesDataiku, Google Vertex AI, Weights & Biases
no first choiceIBM watsonx
Three alternativesAmazon SageMaker, Azure Machine Learning, Databricks
no first choicenothing named
Bold is the first choiceAlternatives are counted; the count opens them.What the answer argued against

05The record

One row per call: the version string exactly as returned, whether the model searched, sources cited, and latency. Full answer text is in the free responses file. Download the record
Seventy-two rows: every prompt, every model, every answer.
PromptModelVersion stringTime (UTC)SearchedSourcesLatency
Direct recommendationClaude Haiku 4.5claude-haiku-4-5-202510012026-09-17 11:07yes99 s
Direct recommendationGPT-5.4 minigpt-5.4-mini-2026-03-172026-09-17 12:58no04 s
Direct recommendationGemini 3.5 Flashgemini-3.5-flash2026-09-17 12:33yes920 s
Direct recommendationPerplexity Sonarsonar2026-09-17 13:10yes206 s
Direct recommendationGrok 4.1 Fastspacexai/grok-4.1-fast-non-reasoning via vertex2026-09-17 13:56yes3716 s
Direct recommendationMistral Smallmistral/mistral-small via mistral2026-09-17 10:12yes118 s
Direct recommendationDeepSeek V4 Flashdeepseek/deepseek-v4-flash via fireworks2026-09-17 13:01yes1016 s
Direct recommendationLlama 4 Maverickmeta/llama-4-maverick via bedrock2026-09-17 09:46yes52 s
Direct recommendationQwen 3.7 Flashalibaba/qwen3.7-flash via alibaba2026-09-17 11:21no033 s
Direct recommendationKimi K2moonshotai/kimi-k2 via novita2026-09-17 11:43yes1022 s
Direct recommendationGLM 4.7 FlashXzai/glm-4.7-flashx via zai2026-09-17 12:15yes9191 s
Direct recommendationMiniMax M2.5minimax/minimax-m2.5 via minimax2026-09-17 12:54yes520 s
ParaphraseClaude Haiku 4.5claude-haiku-4-5-202510012026-09-17 13:26no06 s
ParaphraseGPT-5.4 minigpt-5.4-mini-2026-03-172026-09-17 11:31yes36 s
ParaphraseGemini 3.5 Flashgemini-3.5-flash2026-09-17 12:08yes14120 s
ParaphrasePerplexity Sonarsonar2026-09-17 10:24yes207 s
ParaphraseGrok 4.1 Fastspacexai/grok-4.1-fast-non-reasoning via vertex2026-09-17 10:50yes4016 s
ParaphraseMistral Smallmistral/mistral-small via mistral2026-09-17 14:11yes1213 s
ParaphraseDeepSeek V4 Flashdeepseek/deepseek-v4-flash via fireworks2026-09-17 11:56yes1818 s
ParaphraseLlama 4 Maverickmeta/llama-4-maverick via bedrock2026-09-17 11:30yes53 s
ParaphraseQwen 3.7 Flashalibaba/qwen3.7-flash via alibaba2026-09-17 12:31yes1061 s
ParaphraseKimi K2moonshotai/kimi-k2 via novita2026-09-17 12:51yes1529 s
ParaphraseGLM 4.7 FlashXzai/glm-4.7-flashx via zai2026-09-17 12:05yes2848 s
ParaphraseMiniMax M2.5minimax/minimax-m2.5 via minimax2026-09-17 13:52yes525 s
ComparativeClaude Haiku 4.5claude-haiku-4-5-202510012026-09-17 10:14yes911 s
ComparativeGPT-5.4 minigpt-5.4-mini-2026-03-172026-09-17 09:42yes613 s
ComparativeGemini 3.5 Flashgemini-3.5-flash2026-09-17 09:38yes1126 s
ComparativePerplexity Sonarsonar2026-09-17 13:02yes208 s
ComparativeGrok 4.1 Fastspacexai/grok-4.1-fast-non-reasoning via vertex2026-09-17 12:37yes1911 s
ComparativeMistral Smallmistral/mistral-small via mistral2026-09-17 11:31yes59 s
ComparativeDeepSeek V4 Flashdeepseek/deepseek-v4-flash via fireworks2026-09-17 12:38yes1721 s
ComparativeLlama 4 Maverickmeta/llama-4-maverick via bedrock2026-09-17 13:50yes53 s
ComparativeQwen 3.7 Flashalibaba/qwen3.7-flash via alibaba2026-09-17 13:55yes1048 s
ComparativeKimi K2moonshotai/kimi-k2 via novita2026-09-17 11:29yes1540 s
ComparativeGLM 4.7 FlashXzai/glm-4.7-flashx via zai2026-09-17 13:41yes25293 s
ComparativeMiniMax M2.5minimax/minimax-m2.5 via minimax2026-09-17 10:35yes1028 s
Budget constrainedClaude Haiku 4.5claude-haiku-4-5-202510012026-09-17 10:11yes168 s
Budget constrainedGPT-5.4 minigpt-5.4-mini-2026-03-172026-09-17 11:08yes35 s
Budget constrainedGemini 3.5 Flashgemini-3.5-flash2026-09-17 10:40yes2333 s
Budget constrainedPerplexity Sonarsonar2026-09-17 12:28yes206 s
Budget constrainedGrok 4.1 Fastspacexai/grok-4.1-fast-non-reasoning via vertex2026-09-17 10:49yes4021 s
Budget constrainedMistral Smallmistral/mistral-small via mistral2026-09-17 11:34yes56 s
Budget constrainedDeepSeek V4 Flashdeepseek/deepseek-v4-flash via fireworks2026-09-17 12:52yes2739 s
Budget constrainedLlama 4 Maverickmeta/llama-4-maverick via bedrock2026-09-17 10:08yes52 s
Budget constrainedQwen 3.7 Flashalibaba/qwen3.7-flash via alibaba2026-09-17 14:11no043 s
Budget constrainedKimi K2moonshotai/kimi-k2 via novita2026-09-17 11:10yes1937 s
Budget constrainedGLM 4.7 FlashXzai/glm-4.7-flashx via zai2026-09-17 12:43yes33103 s
Budget constrainedMiniMax M2.5minimax/minimax-m2.5 via minimax2026-09-17 13:34yes20108 s
Scale constrainedClaude Haiku 4.5claude-haiku-4-5-202510012026-09-17 13:50no09 s
Scale constrainedGPT-5.4 minigpt-5.4-mini-2026-03-172026-09-17 12:23no08 s
Scale constrainedGemini 3.5 Flashgemini-3.5-flash2026-09-17 13:46yes825 s
Scale constrainedPerplexity Sonarsonar2026-09-17 11:18yes1517 s
Scale constrainedGrok 4.1 Fastspacexai/grok-4.1-fast-non-reasoning via vertex2026-09-17 13:44yes1512 s
Scale constrainedMistral Smallmistral/mistral-small via mistral2026-09-17 12:12no09 s
Scale constrainedDeepSeek V4 Flashdeepseek/deepseek-v4-flash via fireworks2026-09-17 11:19yes2330 s
Scale constrainedLlama 4 Maverickmeta/llama-4-maverick via bedrock2026-09-17 11:28yes52 s
Scale constrainedQwen 3.7 Flashalibaba/qwen3.7-flash via alibaba2026-09-17 10:59no038 s
Scale constrainedKimi K2moonshotai/kimi-k2 via novita2026-09-17 14:14yes1939 s
Scale constrainedGLM 4.7 FlashXzai/glm-4.7-flashx via zai2026-09-17 11:16yes24133 s
Scale constrainedMiniMax M2.5minimax/minimax-m2.5 via minimax2026-09-17 10:20yes546 s
Negative framingClaude Haiku 4.5claude-haiku-4-5-202510012026-09-17 13:36yes1810 s
Negative framingGPT-5.4 minigpt-5.4-mini-2026-03-172026-09-17 10:55yes58 s
Negative framingGemini 3.5 Flashgemini-3.5-flash2026-09-17 12:39yes2329 s
Negative framingPerplexity Sonarsonar2026-09-17 10:13yes208 s
Negative framingGrok 4.1 Fastspacexai/grok-4.1-fast-non-reasoning via vertex2026-09-17 12:47yes4323 s
Negative framingMistral Smallmistral/mistral-small via mistral2026-09-17 13:42yes1511 s
Negative framingDeepSeek V4 Flashdeepseek/deepseek-v4-flash via fireworks2026-09-17 09:57yes3041 s
Negative framingLlama 4 Maverickmeta/llama-4-maverick via bedrock2026-09-17 12:12yes53 s
Negative framingQwen 3.7 Flashalibaba/qwen3.7-flash via alibaba2026-09-17 14:04no041 s
Negative framingKimi K2moonshotai/kimi-k2 via novita2026-09-17 11:56yes3084 s
Negative framingGLM 4.7 FlashXzai/glm-4.7-flashx via zai2026-09-17 11:40yes2869 s
Negative framingMiniMax M2.5minimax/minimax-m2.5 via minimax2026-09-17 13:56no051 s

Normalization in this category

Every judgment call made between the raw labels and the numbers above, listed so it is visible and reversible.

Category-scoped readings
Domino read as Domino Data Lab
Unresolved, counted raw
AWS DeepComposer
AWS DeepLens
Amazon Lookout for Equipment
Azure OpenAI
Coworker AI
CreateOS
CustomGPT.ai
Databricks Mosaic AI / Databricks ML
Flowise
Google Vertex AI / Gemini Enterprise
HPE Ezmeral
IBM SPSS
IBM watsonx / Watson Machine Learning
Llama
Machine Learning at Scale
Microsoft Azure ML / Azure AI Foundry
Microsoft Azure Machine Learning / AI Foundry
Mistral
NVIDIA AI Enterprise
Open WebUI
Opstream
Palantir Foundry
Palantir Foundry / Gotham
Profound
Python/Tensorflow/PyTorch stack
TensorFlow Extended/TFX
ibl.ai
Discontinued, still offered
No shut-down product was recommended here.

Also in data platform

The other categories of this function, with the leader in each.
← Managed databasesNoSQL →