ML platforms
Asked as “machine learning platform”, and as “MLOps platform for training and deploying models”, on behalf of a mid-market B2B company. 57 first choices recorded across the direct, paraphrase, budget and scale prompts, twelve models each.
Standing · first-choice share
21%
Contested · Databricks Mosaic AI 14%
21Azure Machine Learning14Databricks Mosaic AI11Amazon SageMaker54others
21% of first choices, contested.
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.
The standing
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. Ordered by share.
ProductFirst-choice shareNegative rateLabelsQuadrant
Show the five products at 0%, ordered by negative rate
Bars are the share of first choices, 0 to 100Every product with at least 10 labels here. Every product name links to its product page.
Google Vertex AI is made by Google, whose model Gemini 3.5 Flash is in the set. On the four share prompts that model made it the first choice zero times of 4; the other eleven models five times of 44. Google Colab is made by Google, whose model Gemini 3.5 Flash is in the set. On the four share prompts that model made it the first choice zero times of 4; the other eleven models four times of 44. Lab treatment is defined on the method page; the row is marked, not excluded.
Head to head
All twenty-eight head-to-head pages: the top eight products, each against each
Databricks Mosaic AI vs Google Vertex AIDatabricks Mosaic AI vs DataikuDatabricks Mosaic AI vs TensorFlowDatabricks Mosaic AI vs MLflowAzure Machine Learning vs Amazon SageMakerAzure Machine Learning vs KNIME Analytics PlatformAzure Machine Learning vs Google Vertex AIAzure Machine Learning vs DataikuAzure Machine Learning vs TensorFlowAzure Machine Learning vs MLflowAmazon SageMaker vs KNIME Analytics PlatformAmazon SageMaker vs Google Vertex AIAmazon SageMaker vs DataikuAmazon SageMaker vs TensorFlowAmazon SageMaker vs MLflowKNIME Analytics Platform vs Google Vertex AIKNIME Analytics Platform vs DataikuKNIME Analytics Platform vs TensorFlowKNIME Analytics Platform vs MLflowGoogle Vertex AI vs DataikuGoogle Vertex AI vs TensorFlowGoogle Vertex AI vs MLflowDataiku vs TensorFlowDataiku vs MLflowTensorFlow vs MLflow
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
10
11
12
13Accepted challengerEndorsed leader
0%First-choice share → · lines at 30% share and 25% negative40%
Key
01Azure Machine Learning21%
Azure Machine Learning21%02Databricks Mosaic AI14%
Databricks Mosaic AI14%03Amazon SageMaker11%
Amazon SageMaker11%04Google Vertex AI9%
Google Vertex AI9%05Google Colab9%
Google Colab9%06MLflow7%
MLflow7%07DataRobot4%
DataRobot4%08TensorFlow2%
TensorFlow2%09H2O.ai0%
H2O.ai0%10Dataiku0%
Dataiku0%11Snowflake Cortex AI0%
Snowflake Cortex AI0%12Hugging Face0%
Hugging Face0%13Kubeflow0%
Kubeflow0%What they warned about
Two of twelve models held their first choice under the paraphrase. Claude Haiku 4.5, GPT-5.4 mini, Gemini 3.5 Flash, Perplexity Sonar, Grok 4.20, Mistral Small, 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 AI19%
11 of 57 labels negative · 10 of 12 models · 2 hard negative
“it is often recommended to avoid for small teams or startups due to complexity and cost concerns” Mistral Small, negative prompt
Amazon SageMaker25%
13 of 51 labels negative · 9 of 12 models · 3 hard negative
“Some AI models recommend avoiding this platform if you are a small team, a startup, or just starting out.” Llama 4 Maverick, negative prompt
Kubeflow62%
8 of 13 labels negative · 7 of 12 models · 1 hard negative
“**Kubeflow** | Requires Kubernetes expertise; steep learning curve” Kimi K2, paraphrase prompt
Azure Machine Learning14%
7 of 49 labels negative · 6 of 12 models · 1 hard negative
“models suggesting caution or avoidance unless you have the necessary expertise and budget” Mistral Small, negative prompt
What they cite
Citations exist only for the models that return a source list: fourteen of the twelve in this edition, and all six flagship models on the expanded tier.
Sites the answers cite
72 of 84 answers in this category came back with a source list, from 14 of 12 models: citations where the model returns them, or the search results it consulted. 912 links across 325 sites, every framing counted. Ranked by the number of answers carrying the site or page.
vendor site · G224 answers · 48 citations · 10 models
21 answers · 22 citations · 10 models
20 answers · 29 citations · 10 models
vendor site · G220 answers · 29 citations · 8 models
19 answers · 19 citations · 10 models
16 answers · 20 citations · 10 models
14 answers · 32 citations · 7 models
12 answers · 12 citations · 8 models
vendor site · Databricks12 answers · 12 citations · 7 models
11 answers · 17 citations · 7 models
11 answers · 14 citations · 6 models
vendor site · Dataiku11 answers · 12 citations · 8 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.
19 answers · 10 models
17 answers · 10 models
13 answers · 7 models
13 answers · 10 models
13 answers · 7 models
11 answers · 8 models
11 answers · 7 models
10 answers · 7 models
9 answers · 7 models
9 answers · 4 models
Search against answers
Each company's standing in the answers beside its site's footprint in Google search, one row a site: the products the models named on it with their shares, and the share they add up to; monthly searches on Google, and DataForSEO's estimate of AI search demand (modeled from search signals, directional, not a count of queries to any assistant), for the most-searched of the company's and its products' names (the name is in each row's hover text); estimated monthly organic visits to the site; and its best position in Google's top ten for “best machine learning platform”, “machine learning platform”, “machine learning platforms”. US estimates from DataForSEO and Google's Ads Transparency Center. A small company's site, or a mid-sized company's site for its flagship, is marked company; a product on a large parent's site (Google, Microsoft) has no site figures. A column with no figures for this category is left out, and an empty cell means none were seen, not none exist. Two measurements side by side: neither is read as the cause of the other.
| Company and products | Share | Own site cited | Name searches, Google | AI search demand, est. | Organic visits | Paid search a month, est. | Google ads, last 30 days |
|---|---|---|---|---|---|---|---|
| Azure parent site Azure Machine Learning 21% | 21% | 143 | 301,000 | 47,904 | |||
| Databricks databricks.com Databricks Mosaic AI 14% | 14% | 126 | 135,000 | 8,368 | 689,756 company | $34,664 | 120 |
| Amazon parent site Amazon SageMaker 11%, Amazon SageMaker Canvas 2% | 12% | 155 | 151,000,000 | 1,380,939 | |||
| Google Vertex AI cloud.google.com | 9% | 159 | 5,400 | 226 | 7,405,347 | ||
| Google Colab colab.research.google.com | 9% | 15 | 301,000 | 900 | 240,528 | ||
| MLflow mlflow.org | 7% | 55 | 9,900 | 438 | 6,498 | 1 | |
| DataRobot datarobot.com | 4% | 65 | 5,400 | 169 | 9,791 | 25 | |
| KNIME Analytics Platform knime.com | 4% | 28 | 320 | 3 | 28,171 | 23 | |
| PyTorch pytorch.org | 4% | 17 | 40,500 | 4,030 | 150,980 | ||
| scikit-learn scikit-learn.org | 4% | 8 | 12,100 | 555 | 312,623 |
Google's top ten for the three searches hold 22 sites; 0 of them are among the sites the models cited here. The first result for “best machine learning platform” is anaconda.com.
How 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.ShowHide
How 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.
ShowHideModelDirect“What is the best machine learning platform for a mid-market B2B company?”Paraphrase“Which MLOps platform for training and deploying models would you recommend to a mid-sized B2B company?”Comparative“What are the top machine learning platforms and how do they differ?”Budget-constrained“What is the best machine learning platform for a company with a limited budget?”Scale-constrained“We are a 500 person company evaluating a machine learning platform. What should we look at?”Negative“Which machine learning platforms should I avoid or be cautious about?”
Claude Haiku 4.5Amazon SageMaker, Databricks Mosaic AIWeights & BiasesChangedDatabricks Unified Data Analyticsagainst: Google Vertex AIPyTorch, scikit-learnno first choicenothing named
One alternative
Azure Machine LearningFour alternatives
Amazon SageMaker, Azure Machine Learning, Google Vertex AI, MLflowTwo alternatives
Amazon SageMaker, KNIME Analytics PlatformOne alternative
Google ColabGPT-5.4 miniDatabricks Mosaic AIagainst: Snowflake Cortex AIAmazon SageMaker, Google Vertex AIChangedagainst: KubeflowAmazon SageMakerAmazon SageMaker, Azure Machine Learningagainst: open-source stackno first choicenothing named
Two alternatives
Amazon SageMaker, Google Vertex AIFive alternatives
Azure Machine Learning, Databricks Mosaic AI, Google Vertex AI, H2O.ai, MLflowOne alternative
Google Vertex AIGemini 3.5 FlashPecan AIClearMLChangedagainst: Amazon SageMaker, Kubeflow, Kubernetesno first choiceMLflowagainst: Amazon SageMaker, Google Vertex AIno first choiceagainst: Amazon Bedrock, Amazon SageMaker, Anthropic, Azure AI Foundry, Azure Machine Learning, Databricks Mosaic AI, Google Vertex AI, OpenAI, Snowflake Cortex AI
Five alternatives
Akkio, Azure Machine Learning, Dataiku, Google BigQuery ML, Snowflake Cortex AIFour alternatives
BentoML, Databricks with Mosaic AI / MLflow, Google Vertex AI, Weights & BiasesNine alternatives
BentoML, ClearML, FastAPI, Google Colab, H2O.ai - Open Source Version, KNIME Analytics Platform, Paperspace Gradient, RunPod, Vast.aiPerplexity SonarAzure Machine Learning, Google Vertex AIDatabricks Mosaic AIChangedno first choiceXGBoost, scikit-learnno first choiceagainst: Databricks Mosaic AI, Google Vertex AI, MATLAB, Railway, Weights & Biases
Two alternatives
Amazon Forecast, DataRobotFour alternatives
Amazon SageMaker, Azure Machine Learning, Google Vertex AI, MLflowThree alternatives
H2O.ai, KNIME Analytics Platform, TensorFlowGrok 4.20Azure Machine LearningMLflowChangedagainst: Domino Data Lab, Kubeflowno first choiceGoogle Colabagainst: Amazon SageMaker, Azure Machine Learningno first choiceagainst: Amazon SageMaker, Azure Machine Learning, ChatGPT, DagsHub, Google AI Tools, Google Vertex AI, Hugging Face, Kubeflow
Five alternatives
Amazon SageMaker, DataRobot, Databricks Mosaic AI, Google Vertex AI, H2O.aiFour alternatives
Amazon SageMaker, Azure Machine Learning, Databricks Mosaic AI, Google Vertex AIFive alternatives
H2O-3, Hugging Face Spaces, KNIME Analytics Platform, Kaggle, Open-Source FrameworksFive alternatives
Amazon SageMaker, Azure Machine Learning, Databricks Mosaic AI, Dataiku, Google Vertex AIMistral SmallDatabricks Mosaic AI, Google Vertex AIAmazon SageMaker, Google Vertex AIChangedno first choiceKNIME Analytics Platform, PyCaretno first choiceagainst: Amazon SageMaker, Azure Machine Learning, DagsHub, Google Vertex AI, Kubeflow
One alternative
Amazon SageMakerTwo alternatives
Azure Machine Learning, DataRobotThree alternatives
Azure Machine Learning, BigML, Google AutoMLDeepSeek V4 FlashAzure Machine LearningAzure Machine LearningHeldno first choiceGoogle Colab, Google Vertex AIagainst: Amazon SageMakerno first choiceagainst: Kubeflow, MLflowagainst: Amazon Bedrock, Amazon SageMaker, DataRobot, Databricks Mosaic AI, Dataiku, Google Vertex AI, H2O.ai, SAS Viya
Five alternatives
Amazon SageMaker, DataRobot, Databricks Mosaic AI, Dataiku, Google Vertex AIFour alternatives
Amazon SageMaker, Databricks Mosaic AI, Google Vertex AI, MLflowOne alternative
Databricks Mosaic AIFour alternatives
Azure Machine Learning, Kaggle, PyTorch, TensorFlowThree alternatives
DataRobot, Databricks Mosaic AI, DataikuLlama 4 Maverickno first choiceno first choiceHeldno first choiceAmazon SageMaker Canvasno first choiceagainst: Amazon SageMaker, Google Vertex AI
Two alternatives
Google Colab, Obviously AIQwen 3.7 FlashDataRobotDatabricks Mosaic AI, Domino DataLabChangedno first choiceGoogle Colabagainst: RapidMiner Studio Freeno first choiceagainst: Alteryx, Amazon SageMaker, Apache Spark, Azure Machine Learning, DataRobot, Domino Data Lab, Google Vertex AI, Microsoft PowerBI, RapidMiner, Tableau
Five alternatives
Azure Machine Learning, Databricks Mosaic AI, HubSpot Spot Intelligence, Salesforce Einstein, Snowflake Cortex AITwo alternatives
Azure Machine Learning, Google Vertex AIFive alternatives
Amazon SageMaker Studio Lab, Google Vertex AI, H2O-3, PyTorch, TensorFlowKimi K2Azure Machine Learning, Databricks Mosaic AIagainst: MLflowAzure Machine Learning, MLflowChangedagainst: Amazon SageMaker, Databricks Mosaic AI, Kubeflow, Weights & BiasesAmazon SageMakerPyTorch, TensorFlowno first choiceagainst: AWS, Google's AI/ML Ecosystem
Two alternatives
Amazon SageMaker, Google Vertex AIOne alternative
Google Vertex AIFour alternatives
Azure Machine Learning, Databricks Mosaic AI, Google Vertex AI, Snowflake Cortex AISeven alternatives
Amazon SageMaker, Dataiku, Google AutoML, Google Colab, Google Vertex AI, H2O-3, KNIME Analytics PlatformSix alternatives
Amazon SageMaker, DataRobot, Databricks Mosaic AI, Google Vertex AI, H2O.ai, Weights & BiasesGLM 4.7 FlashXno first choiceAzure Machine LearningChangedno first choiceGoogle Colabno first choiceagainst: ChatGPT, Databricks Mosaic AI, Gemini Enterprise Agent Platform, Google Gemini, Perplexity AI
Two alternatives
DataRobot, MLflowFive alternatives
H2O-3, Hugging Face, KNIME Analytics Platform, Kaggle, MLflowMiniMax M2.5DataRobot, Databricks Mosaic AIAmazon SageMaker, Azure Machine LearningChangedno first choiceKNIME Analytics Platform, RapidMinerno first choiceagainst: Amazon SageMaker, Azure Machine Learning, ClearML, Google Vertex AI, Hugging Face, IBM watsonx.ai, Kubeflow, MLflow, Weights & Biases, ZenML
Two alternatives
Amazon SageMaker, Google Vertex AITwo alternatives
Google Vertex AI, MLflowFour alternatives
Amazon SageMaker, Azure Machine Learning, Databricks Mosaic AI, Google Vertex AITwo alternatives
Akkio, Google Cloud AutoMLBold is the first choiceAlternatives are counted; the count opens them.What the answer argued against
The 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.
| Prompt | Model | Version string | Time (UTC) | Searched | Sources | Latency |
|---|---|---|---|---|---|---|
| Direct recommendation | Claude Haiku 4.5 | claude-haiku-4-5-20251001 | 2026-10-01 10:30 | no | 0 | 4 s |
| Direct recommendation | GPT-5.4 mini | gpt-5.4-mini-2026-03-17 | 2026-10-01 09:26 | yes | 3 | 4 s |
| Direct recommendation | Gemini 3.5 Flash | gemini-3.5-flash | 2026-10-01 11:39 | yes | 14 | 26 s |
| Direct recommendation | Perplexity Sonar | sonar | 2026-10-01 10:29 | yes | 18 | 3 s |
| Direct recommendation | Grok 4.20 | spacexai/grok-4.1-fast-non-reasoning via vertex | 2026-10-01 11:44 | yes | 24 | 12 s |
| Direct recommendation | Mistral Small | mistral/mistral-small via mistral | 2026-10-01 10:35 | yes | 11 | 5 s |
| Direct recommendation | DeepSeek V4 Flash | deepseek/deepseek-v4-flash via deepinfra | 2026-10-01 11:44 | yes | 23 | 25 s |
| Direct recommendation | Llama 4 Maverick | meta/llama-4-maverick via bedrock | 2026-10-01 09:57 | yes | 5 | 1 s |
| Direct recommendation | Qwen 3.7 Flash | alibaba/qwen3.7-flash via alibaba | 2026-10-01 08:33 | yes | 5 | 35 s |
| Direct recommendation | Kimi K2 | moonshotai/kimi-k2 via novita | 2026-10-01 10:57 | yes | 17 | 21 s |
| Direct recommendation | GLM 4.7 FlashX | zai/glm-4.7-flashx via zai | 2026-10-01 09:49 | yes | 15 | 43 s |
| Direct recommendation | MiniMax M2.5 | minimax/minimax-m2.5 via minimax | 2026-10-01 09:10 | yes | 10 | 19 s |
| Paraphrase | Claude Haiku 4.5 | claude-haiku-4-5-20251001 | 2026-10-01 10:15 | yes | 17 | 9 s |
| Paraphrase | GPT-5.4 mini | gpt-5.4-mini-2026-03-17 | 2026-10-01 11:35 | yes | 3 | 6 s |
| Paraphrase | Gemini 3.5 Flash | gemini-3.5-flash | 2026-10-01 09:14 | no | 0 | 15 s |
| Paraphrase | Perplexity Sonar | sonar | 2026-10-01 10:48 | yes | 16 | 3 s |
| Paraphrase | Grok 4.20 | spacexai/grok-4.1-fast-non-reasoning via vertex | 2026-10-01 13:34 | yes | 25 | 10 s |
| Paraphrase | Mistral Small | mistral/mistral-small via mistral | 2026-10-01 13:36 | yes | 5 | 2 s |
| Paraphrase | DeepSeek V4 Flash | deepseek/deepseek-v4-flash via deepinfra | 2026-10-01 08:57 | yes | 24 | 36 s |
| Paraphrase | Llama 4 Maverick | meta/llama-4-maverick via bedrock | 2026-10-01 08:27 | yes | 5 | 1 s |
| Paraphrase | Qwen 3.7 Flash | alibaba/qwen3.7-flash via alibaba | 2026-10-01 11:56 | yes | 20 | 62 s |
| Paraphrase | Kimi K2 | moonshotai/kimi-k2 via novita | 2026-10-01 10:52 | yes | 22 | 25 s |
| Paraphrase | GLM 4.7 FlashX | zai/glm-4.7-flashx via zai | 2026-10-01 09:19 | yes | 24 | 54 s |
| Paraphrase | MiniMax M2.5 | minimax/minimax-m2.5 via minimax | 2026-10-01 12:50 | yes | 5 | 21 s |
| Comparative | Claude Haiku 4.5 | claude-haiku-4-5-20251001 | 2026-10-01 10:12 | yes | 9 | 8 s |
| Comparative | GPT-5.4 mini | gpt-5.4-mini-2026-03-17 | 2026-10-01 12:11 | yes | 6 | 10 s |
| Comparative | Gemini 3.5 Flash | gemini-3.5-flash | 2026-10-01 08:17 | yes | 10 | 23 s |
| Comparative | Perplexity Sonar | sonar | 2026-10-01 10:42 | yes | 16 | 3 s |
| Comparative | Grok 4.20 | spacexai/grok-4.1-fast-non-reasoning via vertex | 2026-10-01 09:14 | yes | 24 | 9 s |
| Comparative | Mistral Small | mistral/mistral-small via mistral | 2026-10-01 10:31 | yes | 8 | 9 s |
| Comparative | DeepSeek V4 Flash | deepseek/deepseek-v4-flash via deepinfra | 2026-10-01 08:36 | yes | 24 | 48 s |
| Comparative | Llama 4 Maverick | meta/llama-4-maverick via bedrock | 2026-10-01 12:52 | yes | 5 | 2 s |
| Comparative | Qwen 3.7 Flash | alibaba/qwen3.7-flash via alibaba | 2026-10-01 11:23 | no | 0 | 27 s |
| Comparative | Kimi K2 | moonshotai/kimi-k2 via novita | 2026-10-01 11:09 | yes | 15 | 33 s |
| Comparative | GLM 4.7 FlashX | zai/glm-4.7-flashx via zai | 2026-10-01 08:02 | no | 0 | 27 s |
| Comparative | MiniMax M2.5 | minimax/minimax-m2.5 via minimax | 2026-10-01 07:37 | yes | 10 | 15 s |
| Budget constrained | Claude Haiku 4.5 | claude-haiku-4-5-20251001 | 2026-10-01 12:40 | no | 0 | 4 s |
| Budget constrained | GPT-5.4 mini | gpt-5.4-mini-2026-03-17 | 2026-10-01 11:41 | yes | 4 | 4 s |
| Budget constrained | Gemini 3.5 Flash | gemini-3.5-flash | 2026-10-01 11:35 | yes | 8 | 18 s |
| Budget constrained | Perplexity Sonar | sonar | 2026-10-01 12:00 | yes | 18 | 4 s |
| Budget constrained | Grok 4.20 | spacexai/grok-4.1-fast-non-reasoning via vertex | 2026-10-01 10:43 | yes | 13 | 7 s |
| Budget constrained | Mistral Small | mistral/mistral-small via mistral | 2026-10-01 11:31 | yes | 5 | 6 s |
| Budget constrained | DeepSeek V4 Flash | deepseek/deepseek-v4-flash via deepinfra | 2026-10-01 08:15 | yes | 19 | 21 s |
| Budget constrained | Llama 4 Maverick | meta/llama-4-maverick via bedrock | 2026-10-01 09:07 | yes | 5 | 2 s |
| Budget constrained | Qwen 3.7 Flash | alibaba/qwen3.7-flash via alibaba | 2026-10-01 09:13 | yes | 10 | 27 s |
| Budget constrained | Kimi K2 | moonshotai/kimi-k2 via novita | 2026-10-01 09:48 | yes | 14 | 26 s |
| Budget constrained | GLM 4.7 FlashX | zai/glm-4.7-flashx via zai | 2026-10-01 08:43 | yes | 16 | 114 s |
| Budget constrained | MiniMax M2.5 | minimax/minimax-m2.5 via minimax | 2026-10-01 12:02 | yes | 5 | 11 s |
| Scale constrained | Claude Haiku 4.5 | claude-haiku-4-5-20251001 | 2026-10-01 08:59 | no | 0 | 6 s |
| Scale constrained | GPT-5.4 mini | gpt-5.4-mini-2026-03-17 | 2026-10-01 12:56 | no | 0 | 7 s |
| Scale constrained | Gemini 3.5 Flash | gemini-3.5-flash | 2026-10-01 13:09 | yes | 12 | 29 s |
| Scale constrained | Perplexity Sonar | sonar | 2026-10-01 10:26 | yes | 17 | 6 s |
| Scale constrained | Grok 4.20 | spacexai/grok-4.1-fast-non-reasoning via vertex | 2026-10-01 11:36 | yes | 14 | 8 s |
| Scale constrained | Mistral Small | mistral/mistral-small via mistral | 2026-10-01 10:06 | no | 0 | 6 s |
| Scale constrained | DeepSeek V4 Flash | deepseek/deepseek-v4-flash via deepinfra | 2026-10-01 12:22 | yes | 22 | 41 s |
| Scale constrained | Llama 4 Maverick | meta/llama-4-maverick via bedrock | 2026-10-01 09:53 | yes | 5 | 3 s |
| Scale constrained | Qwen 3.7 Flash | alibaba/qwen3.7-flash via alibaba | 2026-10-01 09:45 | no | 0 | 31 s |
| Scale constrained | Kimi K2 | moonshotai/kimi-k2 via novita | 2026-10-01 11:23 | no | 0 | 25 s |
| Scale constrained | GLM 4.7 FlashX | zai/glm-4.7-flashx via zai | 2026-10-01 12:15 | no | 0 | 48 s |
| Scale constrained | MiniMax M2.5 | minimax/minimax-m2.5 via minimax | 2026-10-01 10:20 | yes | 5 | 25 s |
| Negative framing | Claude Haiku 4.5 | claude-haiku-4-5-20251001 | 2026-10-01 08:24 | yes | 9 | 8 s |
| Negative framing | GPT-5.4 mini | gpt-5.4-mini-2026-03-17 | 2026-10-01 08:18 | yes | 4 | 8 s |
| Negative framing | Gemini 3.5 Flash | gemini-3.5-flash | 2026-10-01 10:59 | yes | 18 | 24 s |
| Negative framing | Perplexity Sonar | sonar | 2026-10-01 10:31 | yes | 19 | 3 s |
| Negative framing | Grok 4.20 | spacexai/grok-4.1-fast-non-reasoning via vertex | 2026-10-01 11:32 | yes | 23 | 8 s |
| Negative framing | Mistral Small | mistral/mistral-small via mistral | 2026-10-01 09:13 | yes | 5 | 4 s |
| Negative framing | DeepSeek V4 Flash | deepseek/deepseek-v4-flash via deepinfra | 2026-10-01 13:52 | yes | 24 | 29 s |
| Negative framing | Llama 4 Maverick | meta/llama-4-maverick via bedrock | 2026-10-01 13:23 | yes | 5 | 2 s |
| Negative framing | Qwen 3.7 Flash | alibaba/qwen3.7-flash via alibaba | 2026-10-01 08:50 | no | 0 | 32 s |
| Negative framing | Kimi K2 | moonshotai/kimi-k2 via novita | 2026-10-01 12:41 | yes | 25 | 31 s |
| Negative framing | GLM 4.7 FlashX | zai/glm-4.7-flashx via zai | 2026-10-01 08:26 | yes | 25 | 20 s |
| Negative framing | MiniMax M2.5 | minimax/minimax-m2.5 via minimax | 2026-10-01 11:30 | yes | 19 | 32 s |
Normalization in this category
Every judgment call made between the raw labels and the numbers above, listed so it is visible and reversible.
ShowHide
Category-scoped readings
AWS SageMaker Studio read as Amazon SageMaker
Databricks read as Databricks Mosaic AI
Databricks (Mosaic AI) read as Databricks Mosaic AI
Databricks Community Edition read as Databricks Mosaic AI
Databricks Data Intelligence Platform read as Databricks Mosaic AI
Databricks Machine Learning read as Databricks Mosaic AI
Domino read as Domino Data Lab
KNIME read as KNIME Analytics Platform
Palantir read as Palantir Foundry
Snowflake read as Snowflake Cortex AI
Snowflake (Cortex ML & Snowpark) read as Snowflake Cortex AI
Snowflake Cortex read as Snowflake Cortex AI
Snowflake ML read as Snowflake Cortex AI
Unresolved, counted raw
Apache Spark MLlib
CometML
CymonixIQ+
Databricks Unified Data Analytics
Databricks with Mosaic AI / MLflow
Domino DataLab
Feast
GCP Vertex
Google AI Tools (e.g., Bard/Gemini)
Google Cloud's unified platform
Google's AI/ML Ecosystem
Google\u2019s AI infrastructure
H2O.ai - Open Source Version
Hex
HubSpot Spot Intelligence
Hyperstack
Le Chat
MATLAB
Microsoft Azure AI Foundry
Microsoft PowerBI
Mosaic AI
Open-Source Frameworks (e.g., scikit-learn, TensorFlow, PyTorch)
Paperspace Gradient
Perplexity AI
Pi.ai
Snowflake Data Cloud
Tecton
Vertex AI Studio
XGBoost
open-source stack
vLLM
Discontinued, still offered
No shut-down product was recommended here.