ML platforms for small business buyers
Asked as “machine learning platform”, and as “MLOps platform for training and deploying models”, on behalf of a small B2B company. 64 first choices recorded across the direct, paraphrase, budget and scale prompts, fourteen models each.
Standing · first-choice share
19%
Contested · Google Colab 9%
19Google Vertex AI09Google Colab08Akkio64others
19% of first choices, contested.
Since September 2026=heldSince September 2026: 17% → 17%, ±0 points. Inside the 11-point floor: within noise. Read over the models both editions asked.Google Vertex AI held the lead, ±0 points on 17%, inside the 11-point floor.
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 business
Google Vertex AI19%Contested=heldSince September 2026: 17% → 17%, ±0 points. Inside the 11-point floor: within noise. Read over the models both editions asked.This pageMid-market
Azure Machine Learning21%Contested↗new leaderNew leader since September 2026: Azure Machine Learning (18%) replaces Databricks Mosaic AI (19% then, 14% now), 4 points clear, inside the 11-point floor.Standing →Enterprise
Databricks Mosaic AI44%Clear leader▲+10Since September 2026: 33% → 44%, +10 points. Inside the 11-point floor: within noise. Read over the models both editions asked.Standing →
Google Vertex AI19%Contested=heldSince September 2026: 17% → 17%, ±0 points. Inside the 11-point floor: within noise. Read over the models both editions asked.This pageMid-marketAzure Machine Learning21%Contested↗new leaderNew leader since September 2026: Azure Machine Learning (18%) replaces Databricks Mosaic AI (19% then, 14% now), 4 points clear, inside the 11-point floor.Standing →EnterpriseDatabricks Mosaic AI44%Clear leader▲+10Since September 2026: 33% → 44%, +10 points. Inside the 11-point floor: within noise. Read over the models both editions asked.Standing →The standing
Share is the count of first choices across the direct, paraphrase, budget and scale prompts, over all fourteen models, for a small B2B company. Ordered by share.
ProductFirst-choice shareNegative rateLabelsQuadrantSince September 2026
01Google Vertex AI Lab in the set19%27%55criticized challenger=heldSince September 2026: 17% → 17%, ±0 points. Inside the 11-point floor: within noise. Read over the models both editions asked.17% → 17%
Google Vertex AI Lab in the set19%27%55criticized challenger=heldSince September 2026: 17% → 17%, ±0 points. Inside the 11-point floor: within noise. Read over the models both editions asked.17% → 17%02
KNIME Analytics Platform8%0%20accepted challenger▼−2Since September 2026: 8% → 6%, −2 points. Inside the 11-point floor: within noise. Read over the models both editions asked.8% → 6%
KNIME Analytics Platform8%0%20accepted challenger▼−2Since September 2026: 8% → 6%, −2 points. Inside the 11-point floor: within noise. Read over the models both editions asked.8% → 6%03
Amazon SageMaker8%42%52criticized challenger▲+6Since September 2026: 2% → 8%, +6 points. Inside the 11-point floor: within noise. Read over the models both editions asked.2% → 8%
Amazon SageMaker8%42%52criticized challenger▲+6Since September 2026: 2% → 8%, +6 points. Inside the 11-point floor: within noise. Read over the models both editions asked.2% → 8%04
MLflow6%0%26accepted challenger▼−4Since September 2026: 10% → 6%, −4 points. Inside the 11-point floor: within noise. Read over the models both editions asked.10% → 6%
MLflow6%0%26accepted challenger▼−4Since September 2026: 10% → 6%, −4 points. Inside the 11-point floor: within noise. Read over the models both editions asked.10% → 6%05Azure Machine Learning5%27%48criticized challenger▼−6Since September 2026: 10% → 4%, −6 points. Inside the 11-point floor: within noise. Read over the models both editions asked.10% → 4%
Azure Machine Learning5%27%48criticized challenger▼−6Since September 2026: 10% → 4%, −6 points. Inside the 11-point floor: within noise. Read over the models both editions asked.10% → 4%06
Hugging Face5%7%14accepted challenger▲+4Since September 2026: 0% → 4%, +4 points. Inside the 11-point floor: within noise. Read over the models both editions asked.0% → 4%
Hugging Face5%7%14accepted challenger▲+4Since September 2026: 0% → 4%, +4 points. Inside the 11-point floor: within noise. Read over the models both editions asked.0% → 4%07
TensorFlow3%14%14accepted challenger▲+2Since September 2026: 2% → 4%, +2 points. Inside the 11-point floor: within noise. Read over the models both editions asked.2% → 4%
TensorFlow3%14%14accepted challenger▲+2Since September 2026: 2% → 4%, +2 points. Inside the 11-point floor: within noise. Read over the models both editions asked.2% → 4%08Databricks Mosaic AI3%41%32criticized challenger=heldSince September 2026: 4% → 4%, ±0 points. Inside the 11-point floor: within noise. Read over the models both editions asked.4% → 4%
Databricks Mosaic AI3%41%32criticized challenger=heldSince September 2026: 4% → 4%, ±0 points. Inside the 11-point floor: within noise. Read over the models both editions asked.4% → 4%09
H2O.ai2%0%14accepted challenger=heldSince September 2026: 2% → 2%, ±0 points. Inside the 11-point floor: within noise. Read over the models both editions asked.2% → 2%
H2O.ai2%0%14accepted challenger=heldSince September 2026: 2% → 2%, ±0 points. Inside the 11-point floor: within noise. Read over the models both editions asked.2% → 2%10
Dataiku2%8%13accepted challenger▼−2Since September 2026: 2% → 0%, −2 points. Inside the 11-point floor: within noise. Read over the models both editions asked.2% → 0%
Dataiku2%8%13accepted challenger▼−2Since September 2026: 2% → 0%, −2 points. Inside the 11-point floor: within noise. Read over the models both editions asked.2% → 0%11Weights & Biases2%8%13accepted challenger▲+2Since September 2026: 0% → 2%, +2 points. Inside the 11-point floor: within noise. Read over the models both editions asked.0% → 2%
12
DataRobot2%50%20criticized challenger▲+2Since September 2026: 0% → 2%, +2 points. Inside the 11-point floor: within noise. Read over the models both editions asked.0% → 2%
DataRobot2%50%20criticized challenger▲+2Since September 2026: 0% → 2%, +2 points. Inside the 11-point floor: within noise. Read over the models both editions asked.0% → 2%Show the two products at 0%, ordered by negative rate
14
Kubeflow0%77%13criticized challenger=heldSince September 2026: 0% → 0%, ±0 points. Inside the 11-point floor: within noise. Read over the models both editions asked.0% → 0%
Kubeflow0%77%13criticized challenger=heldSince September 2026: 0% → 0%, ±0 points. Inside the 11-point floor: within noise. Read over the models both editions asked.0% → 0%13
RapidMiner0%0%10accepted challenger=heldSince September 2026: 0% → 0%, ±0 points. Inside the 11-point floor: within noise. Read over the models both editions asked.0% → 0%
RapidMiner0%0%10accepted challenger=heldSince September 2026: 0% → 0%, ±0 points. Inside the 11-point floor: within noise. Read over the models both editions asked.0% → 0%The floor is 11 points of share, measured: how far the models move a leader on their own when the same questions are asked twice with nothing changed. A larger change is movement; a smaller one is noise, and both are shown. Movement is read over the twelve models both editions asked; GPT-6 Luna, Muse Glimmer 30B joined this edition and are in the standing but not yet in the comparison. How the floor is measured
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 thirteen models twelve times of 52. 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
Azure Machine Learning vs Google ColabAzure Machine Learning vs MLflowAzure Machine Learning vs DataRobotAzure Machine Learning vs TensorFlowDatabricks Mosaic AI vs Amazon SageMakerDatabricks Mosaic AI vs Google Vertex AIDatabricks Mosaic AI vs Google ColabDatabricks Mosaic AI vs MLflowDatabricks Mosaic AI vs DataRobotDatabricks Mosaic AI vs TensorFlowAmazon SageMaker vs Google Vertex AIAmazon SageMaker vs Google ColabAmazon SageMaker vs MLflowAmazon SageMaker vs DataRobotAmazon SageMaker vs TensorFlowGoogle Vertex AI vs Google ColabGoogle Vertex AI vs MLflowGoogle Vertex AI vs DataRobotGoogle Vertex AI vs TensorFlowGoogle Colab vs MLflowGoogle Colab vs DataRobotGoogle Colab vs TensorFlowMLflow vs DataRobotMLflow vs TensorFlowDataRobot vs TensorFlow
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 →
01020304050607080910W11
121314Accepted challengerEndorsed leader
0%First-choice share → · lines at 30% share and 25% negative30%
Key
01Google Vertex AI19%
Google Vertex AI19%02KNIME Analytics Platform8%
KNIME Analytics Platform8%03Amazon SageMaker8%
Amazon SageMaker8%04MLflow6%
MLflow6%05Azure Machine Learning5%
Azure Machine Learning5%06Hugging Face5%
Hugging Face5%07TensorFlow3%
TensorFlow3%08Databricks Mosaic AI3%
Databricks Mosaic AI3%09H2O.ai2%
H2O.ai2%10Dataiku2%
Dataiku2%11Weights & Biases2%
12DataRobot2%
DataRobot2%13RapidMiner0%
RapidMiner0%14Kubeflow0%
Kubeflow0%What they warned about
Two of fourteen models held their first choice under the paraphrase. Claude Haiku 4.5, GPT-5.4 mini, Gemini 3.5 Flash, Perplexity Sonar, Grok 4.1 Fast, Mistral Small, DeepSeek V4 Flash, Qwen 3.7 Flash, Kimi K2, GLM 4.7 FlashX, MiniMax M2.5 and Muse Glimmer 30B 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.
Amazon SageMaker42%
22 of 52 labels negative · 12 of 14 models · 11 hard negative
““Avoid or be very cautious with enterprise‑focused, full‑featured cloud ML services (SageMaker, Azure ML, Google Cloud AI Platform, IBM Watson)”” Muse Glimmer 30B, negative prompt
Azure Machine Learning27%
13 of 48 labels negative · 10 of 14 models · 8 hard negative
““Avoid or be very cautious with enterprise‑focused, full‑featured cloud ML services (SageMaker, Azure ML, Google Cloud AI Platform, IBM Watson)”” Muse Glimmer 30B, negative prompt
Google Vertex AI27%
15 of 55 labels negative · 10 of 14 models · 8 hard negative
“**Why Avoid:** These platforms are designed for large enterprises with existing infrastructure integration and dedicated engineering departments.” Qwen 3.7 Flash, negative prompt
Databricks Mosaic AI41%
13 of 32 labels negative · 8 of 14 models · 6 hard negative
“Examples: Full-scale Databricks, custom AWS SageMaker or Azure ML pipelines, Kubeflow, Palantir. ... Why to avoid/be cautious” Gemini 3.5 Flash, negative prompt
What they cite
Citations exist only for the models that return a source list: fourteen of the fourteen in this edition, and all six flagship models on the expanded tier.
Sites the answers cite
70 of 84 answers in this category came back with a source list, from 14 of 14 models: citations where the model returns them, or the search results it consulted. 949 links across 287 sites, every framing counted. Ranked by the number of answers carrying the site or page. 56 of the 252 answers across every segment cited this index's own page for the category; the method page measures whether that reading tilts an answer.
vendor site · G245 answers · 99 citations · 12 models
25 answers · 26 citations · 10 models
24 answers · 29 citations · 10 models
vendor site · G223 answers · 31 citations · 10 models
20 answers · 28 citations · 11 models
19 answers · 23 citations · 8 models
vendor site · FitGap19 answers · 23 citations · 10 models
15 answers · 30 citations · 8 models
14 answers · 14 citations · 7 models
vendor site · DagsHub11 answers · 11 citations · 9 models
10 answers · 11 citations · 7 models
10 answers · 10 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.
29 answers · 12 models
24 answers · 10 models
22 answers · 10 models
16 answers · 9 models
14 answers · 7 models
11 answers · 8 models
11 answers · 9 models
10 answers · 5 models
10 answers · 8 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 |
|---|---|---|---|---|---|---|---|
| Google Vertex AI cloud.google.com | 19% | 159 | 5,400 | 226 | 7,405,347 | ||
| Google Colab colab.research.google.com | 9% | 15 | 301,000 | 900 | 240,528 | ||
| KNIME Analytics Platform knime.com | 8% | 28 | 320 | 3 | 28,171 | 23 | |
| Akkio akkio.com | 8% | 10 | 1,000 | 10 | 2,874 | ||
| Amazon parent site Amazon SageMaker 8% | 8% | 155 | 151,000,000 | 1,380,939 | |||
| MLflow mlflow.org | 6% | 55 | 9,900 | 438 | 6,498 | 1 | |
| Azure parent site Azure Machine Learning 5%, Azure OpenAI Service 2% | 6% | 143 | 301,000 | 47,904 | |||
| Hugging Face huggingface.co Hugging Face 5%, Hugging Face Inference Endpoints 2% | 6% | 23 | 135,000 | 1,844 | 1,133,354 | 2 | |
| TensorFlow tensorflow.org | 3% | 23 | 18,100 | 2,745 | 198,535 | 3 | |
| Databricks databricks.com Databricks Mosaic AI 3% | 3% | 126 | 135,000 | 8,368 | 689,756 company | $34,664 | 120 |
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 small B2B company?”Paraphrase“Which MLOps platform for training and deploying models would you recommend to a small business that sells to other businesses?”Comparative“What are the top machine learning platforms for a small team and how do they differ?”Budget-constrained“What is the best machine learning platform for a small company with a tight budget?”Scale-constrained“We are a 30 person company evaluating a machine learning platform. What should we look at?”Negative“Which machine learning platforms should a small business avoid or be cautious about?”
Claude Haiku 4.5AkkioAmazon SageMaker, Google Vertex AIChangedagainst: KubeflowKNIME Analytics Platformagainst: Amazon SageMaker, Azure Machine Learning, Google Vertex AIGoogle Colab, TensorFlow, scikit-learnno first choiceagainst: Amazon SageMaker, Azure Machine Learning, Databricks Lakehouse Platform, Google Vertex AI, TensorFlow
Two alternatives
H2O.ai, LevityOne alternative
MLflowThree alternatives
Anyscale, Hugging Face, scikit-learnOne alternative
Google Vertex AIGPT-5.4 miniDatabricks Mosaic AIAmazon SageMakerChangedAmazon SageMaker, Google Vertex AIGoogle Vertex AI, open-source tools + cheap cloud computeagainst: Databricks Mosaic AIno first choicenothing named
Two alternatives
Amazon SageMaker, Snowflake Cortex AITwo alternatives
Azure Machine Learning, Databricks Mosaic AIThree alternatives
Azure Machine Learning, Databricks Mosaic AI, Hugging Face AutoTrain / Hugging Face HubTwo alternatives
Amazon SageMaker, Azure Machine LearningGemini 3.5 FlashAkkio, Pecan AIagainst: Alteryxno first choiceChangedModalagainst: Amazon SageMaker, Google Vertex AIDeepInfra, Fireworks AIagainst: Anthropic, DataRobot, OpenAI, SASBaseten, Modalagainst: Amazon SageMaker, Dataiku, Google Vertex AI, Kubeflowagainst: Amazon SageMaker, Azure Machine Learning, ChatGPT, Databricks Mosaic AI, Kubeflow, LLaMA, Mistral, Palantir Foundry
Five alternatives
AWS SageMaker Autopilot, Airtable, Google Vertex AI, KNIME Analytics Platform, MindStudioFour alternatives
Baseten, Hugging Face, TrueFoundry, Weights & Biases (W&B) / W&B WeaveTen alternatives
Akkio, AutoGluon, Cloudflare Workers AI, Hugging Face, MindsDB, Modal, Obviously AI, PyCaret, RunPod, Vast.aiEight alternatives
Akkio, Anyscale, ClearML, Google AutoML, MLflow, Obviously AI, Replicate, Weights & BiasesPerplexity SonarAkkioClearMLChangedDeepnoteagainst: Databricks Mosaic AIPyTorch, TensorFlowno first choiceagainst: Amazon SageMaker, Azure Machine Learning, Databricks Mosaic AI, Domino Data Lab, Google Cloud AI / Vertex AI, IBM Watson, MATLAB-based ML tooling, Oracle AI, SAP AI
Two alternatives
Amazon SageMaker, DeepnoteTwo alternatives
Databricks Mosaic AI, MLflowFive alternatives
Amazon SageMaker, Azure Machine Learning, DVC, Google Cloud AutoML & Vertex AI, MLflowTwo alternatives
Amazon Forecast, Google Cloud AutoML & Vertex AIGrok 4.1 FastAmazon SageMaker, Google Vertex AIagainst: Databricks Mosaic AI, PyTorch, TensorFlowMLflowChangedagainst: Amazon SageMakerH2O.ai, KNIME Analytics Platform, MLflowGoogle Colabno first choiceagainst: Amazon SageMaker, Azure Machine Learning, Databricks Mosaic AI, Google Vertex AI, IBM Watson, Kubeflow, SAS Viya, Snowflake Cortex AI
Two alternatives
Azure Machine Learning, DataRobotThree alternatives
ClearML, Google Vertex AI, Weights & BiasesFive alternatives
Amazon SageMaker, Azure Machine Learning, Databricks Mosaic AI, Google Vertex AI, RapidMinerSix alternatives
Amazon SageMaker, Amazon SageMaker Studio Lab, Google Vertex AI, Hugging Face Spaces, Kaggle, Local open-sourceMistral SmallAzure OpenAI ServiceHugging Face, Hugging Face Inference EndpointsChangedAzure Machine Learning, Google Vertex AIAkkio, Google Cloud AI/ML Toolsno first choiceagainst: Amazon SageMaker, Azure Machine Learning, Google Vertex AI, IBM Watson, Kubeflow, Oracle AI, SAP AI
Two alternatives
Oracle Data Science Cloud Service, TensorFlowEight alternatives
Amazon SageMaker, BentoML, FastAPI, Fly.io, Google Vertex AI, MLflow, Railway, Seldon CoreFive alternatives
Amazon SageMaker, BentoML, MLflow, PyTorch, TensorFlowDeepSeek V4 FlashAkkioagainst: Amazon SageMaker, DataRobotMLflowChangedagainst: ValohaiH2O.ai, KNIME Analytics Platformagainst: DataRobot, Databricks Mosaic AIGoogle Colab, KNIME Analytics Platformagainst: Amazon SageMakerno first choiceagainst: Weights & Biasesagainst: Amazon SageMaker, Azure Machine Learning, DataRobot, Databricks Mosaic AI, Google Vertex AI, Replit
Four alternatives
Azure Machine Learning, Google Vertex AI, H2O.ai, ZapierFive alternatives
Amazon SageMaker, Azure Machine Learning, DataRobot, Google Vertex AI, H2O.aiFour alternatives
Amazon SageMaker, Azure Machine Learning, Dataiku, Google Vertex AISix alternatives
Dataiku, Google Vertex AI, PyTorch, SageMaker Studio Lab, TensorFlow, scikit-learnSeven alternatives
Amazon SageMaker, Databricks Mosaic AI, Dataiku, Google Vertex AI, H2O.ai, KNIME Analytics Platform, MLflowLlama 4 Maverickno first choiceno first choiceHeldMLflowKNIME Analytics Platformno first choiceagainst: Amazon SageMaker, Azure Machine Learning, Google Vertex AI, IBM Watson, Kubeflow, Oracle AI, SAP AI
Three alternatives
Azure Machine Learning, DVC, Databricks Mosaic AITwo alternatives
BigML, DataikuQwen 3.7 FlashH2O.aiagainst: DataRobotWeights & BiasesChangedagainst: Amazon SageMaker, Azure Machine LearningDataikuGoogle Colab, KNIME Analytics PlatformDatabricks Mosaic AIagainst: Kubeflowagainst: Amazon SageMaker, Azure Machine Learning, DagsHub, Google Vertex AI, IBM Watson, Kubeflow, Oracle AI, SAP AI
Four alternatives
Amazon SageMaker Canvas, Clay.com, Microsoft Power BI, TableauFour alternatives
BentoML, DVC, MLflow, Seldon CoreFour alternatives
Amazon SageMaker Canvas, Google Vertex AI, KNIME Analytics Platform, Weights & BiasesFour alternatives
Azure Machine Learning, H2O.ai, Kaggle, RapidMiner Studio FreeFour alternatives
LangChain-based hosting, Snowflake Cortex AI, Vercel AI Stack, Weights & BiasesKimi K2Azure Machine Learning, Google Vertex AIagainst: Amazon SageMakerGoogle Vertex AIChangedagainst: DataRobot, Databricks Mosaic AI, Domino Data LabGoogle BigQuery ML, Google Vertex AIGoogle Vertex AIno first choiceagainst: Amazon SageMaker, Azure Machine Learning, DataRobot, Databricks Mosaic AI, Domino Data Lab, Google Vertex AI
Three alternatives
BigML, Levity, MonkeyLearnTwo alternatives
Amazon SageMaker, MLflowEight alternatives
Amazon SageMaker, Azure Machine Learning, Databricks Mosaic AI, Hugging Face, KNIME Analytics Platform, MLflow, Snowflake Cortex AI, Weights & BiasesThree alternatives
Amazon SageMaker, Azure Machine Learning, TensorFlowSeven alternatives
Amazon SageMaker, Azure Machine Learning, Databricks Mosaic AI, Deepnote, Google Vertex AI, MLflow, Weights & BiasesGLM 4.7 FlashXHugging Faceagainst: DataRobotMLflowChangedno first choiceGoogle Colabagainst: Qlik AutoMLno first choiceagainst: Amazon SageMaker, DataRobot, Google Vertex AI
Three alternatives
Azure Machine Learning, Kimola Cognitive, MonkeyLearnTwo alternatives
Google Vertex AI, Weights & BiasesNine alternatives
Amazon SageMaker, Azure Machine Learning, DataRobot, Dataiku, Google Vertex AI, H2O.ai, KNIME Analytics Platform, Pecan AI, RapidMinerTen alternatives
Beam, DVC, KNIME Analytics Platform, MLflow, Modal, Nyckel, Replicate, RunPod, Teachable Machine, TensorFlowMiniMax M2.5Azure Machine Learning, Google Vertex AIDataRobot, Google Vertex AIChangedAzure Machine Learning, Google Vertex AIAmazon SageMaker, Google Vertex AIno first choiceagainst: Amazon SageMakernothing named
Two alternatives
BigML, TensorFlowThree alternatives
Azure Machine Learning, H2O.ai, MLflowThree alternatives
Amazon SageMaker, Saturn Cloud, TensorFlowTwo alternatives
Hugging Face, StreamlitGPT-6 LunaGoogle Vertex AIagainst: Databricks Mosaic AIGoogle Vertex AIHeldGoogle Vertex AIagainst: Databricks Mosaic AI, Hugging FaceHugging Faceno first choiceagainst: Copilot, Google Gemini
Two alternatives
Azure Machine Learning, SageMaker AITwo alternatives
Azure Machine Learning, SageMaker AIFour alternatives
Amazon SageMaker, Azure Machine Learning, MLflow, ModalTwo alternatives
Amazon SageMaker, Google ColabMuse Glimmer 30BBigML, KNIME Analytics PlatformMLflowChangedagainst: Amazon SageMaker, Azure Machine Learning, Databricks Mosaic AI, Google Vertex AIDeepnote, MLflowagainst: Amazon SageMaker, Google Vertex AI, KubeflowGoogle Colab, KNIME Analytics Platformagainst: DataRobotAmazon SageMaker, Azure Machine Learning, Dataiku, Google Vertex AIagainst: Amazon SageMaker, Azure Machine Learning, DagsHub, Google Vertex AI, IBM Watson, Kubeflow
Five alternatives
Akkio, Amazon SageMaker Canvas, Azure Machine Learning, Google Cloud AutoML, Obviously AITwo alternatives
Iterative.ai, Weights & BiasesSix alternatives
Azure Machine Learning, Azure Machine Learning Studio, DVC, Databricks Mosaic AI, KNIME Analytics Platform, RapidMinerThree alternatives
H2O-3, PyTorch, TensorFlowBold 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
Eighty-four 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 13:12 | yes | 9 | 7 s |
| Direct recommendation | GPT-5.4 mini | gpt-5.4-mini-2026-03-17 | 2026-10-01 10:10 | yes | 3 | 5 s |
| Direct recommendation | Gemini 3.5 Flash | gemini-3.5-flash | 2026-10-01 10:03 | yes | 12 | 21 s |
| Direct recommendation | Perplexity Sonar | sonar | 2026-10-01 07:39 | yes | 18 | 2 s |
| Direct recommendation | Grok 4.1 Fast | spacexai/grok-4.1-fast-non-reasoning via vertex | 2026-10-01 13:32 | yes | 23 | 8 s |
| Direct recommendation | Mistral Small | mistral/mistral-small via mistral | 2026-10-01 11:55 | yes | 5 | 3 s |
| Direct recommendation | DeepSeek V4 Flash | deepseek/deepseek-v4-flash via deepinfra | 2026-10-01 08:49 | yes | 24 | 22 s |
| Direct recommendation | Llama 4 Maverick | meta/llama-4-maverick via bedrock | 2026-10-01 12:45 | yes | 5 | 1 s |
| Direct recommendation | Qwen 3.7 Flash | alibaba/qwen3.7-flash via alibaba | 2026-10-01 12:56 | no | 0 | 32 s |
| Direct recommendation | Kimi K2 | moonshotai/kimi-k2 via novita | 2026-10-01 13:46 | yes | 19 | 21 s |
| Direct recommendation | GLM 4.7 FlashX | zai/glm-4.7-flashx via zai | 2026-10-01 12:16 | yes | 24 | 93 s |
| Direct recommendation | MiniMax M2.5 | minimax/minimax-m2.5 via minimax | 2026-10-01 12:47 | yes | 8 | 23 s |
| Direct recommendation | GPT-6 Luna | gpt-6-luna | 2026-10-01 09:29 | yes | 5 | 14 s |
| Direct recommendation | Muse Glimmer 30B | meta/muse-glimmer-30b via togetherai | 2026-10-01 11:10 | yes | 22 | 25 s |
| Paraphrase | Claude Haiku 4.5 | claude-haiku-4-5-20251001 | 2026-10-01 11:54 | no | 0 | 5 s |
| Paraphrase | GPT-5.4 mini | gpt-5.4-mini-2026-03-17 | 2026-10-01 13:26 | yes | 3 | 4 s |
| Paraphrase | Gemini 3.5 Flash | gemini-3.5-flash | 2026-10-01 13:50 | no | 0 | 8 s |
| Paraphrase | Perplexity Sonar | sonar | 2026-10-01 10:01 | yes | 16 | 3 s |
| Paraphrase | Grok 4.1 Fast | spacexai/grok-4.1-fast-non-reasoning via vertex | 2026-10-01 12:39 | yes | 23 | 10 s |
| Paraphrase | Mistral Small | mistral/mistral-small via mistral | 2026-10-01 10:23 | no | 0 | 4 s |
| Paraphrase | DeepSeek V4 Flash | deepseek/deepseek-v4-flash via deepinfra | 2026-10-01 12:11 | yes | 22 | 30 s |
| Paraphrase | Llama 4 Maverick | meta/llama-4-maverick via bedrock | 2026-10-01 08:13 | yes | 5 | 2 s |
| Paraphrase | Qwen 3.7 Flash | alibaba/qwen3.7-flash via alibaba | 2026-10-01 08:19 | yes | 14 | 96 s |
| Paraphrase | Kimi K2 | moonshotai/kimi-k2 via novita | 2026-10-01 12:12 | yes | 19 | 18 s |
| Paraphrase | GLM 4.7 FlashX | zai/glm-4.7-flashx via zai | 2026-10-01 12:11 | yes | 23 | 72 s |
| Paraphrase | MiniMax M2.5 | minimax/minimax-m2.5 via minimax | 2026-10-01 08:50 | yes | 5 | 12 s |
| Paraphrase | GPT-6 Luna | gpt-6-luna | 2026-10-01 08:55 | yes | 3 | 12 s |
| Paraphrase | Muse Glimmer 30B | meta/muse-glimmer-30b via togetherai | 2026-10-01 09:04 | yes | 15 | 30 s |
| Comparative | Claude Haiku 4.5 | claude-haiku-4-5-20251001 | 2026-10-01 09:45 | yes | 9 | 8 s |
| Comparative | GPT-5.4 mini | gpt-5.4-mini-2026-03-17 | 2026-10-01 09:54 | yes | 5 | 11 s |
| Comparative | Gemini 3.5 Flash | gemini-3.5-flash | 2026-10-01 09:42 | yes | 17 | 27 s |
| Comparative | Perplexity Sonar | sonar | 2026-10-01 11:42 | yes | 17 | 4 s |
| Comparative | Grok 4.1 Fast | spacexai/grok-4.1-fast-non-reasoning via vertex | 2026-10-01 12:20 | yes | 20 | 9 s |
| Comparative | Mistral Small | mistral/mistral-small via mistral | 2026-10-01 08:07 | yes | 15 | 9 s |
| Comparative | DeepSeek V4 Flash | deepseek/deepseek-v4-flash via deepinfra | 2026-10-01 13:52 | yes | 24 | 49 s |
| Comparative | Llama 4 Maverick | meta/llama-4-maverick via bedrock | 2026-10-01 12:31 | yes | 9 | 2 s |
| Comparative | Qwen 3.7 Flash | alibaba/qwen3.7-flash via alibaba | 2026-10-01 13:25 | yes | 15 | 51 s |
| Comparative | Kimi K2 | moonshotai/kimi-k2 via novita | 2026-10-01 10:23 | yes | 17 | 30 s |
| Comparative | GLM 4.7 FlashX | zai/glm-4.7-flashx via zai | 2026-10-01 07:44 | yes | 18 | 52 s |
| Comparative | MiniMax M2.5 | minimax/minimax-m2.5 via minimax | 2026-10-01 10:42 | yes | 24 | 28 s |
| Comparative | GPT-6 Luna | gpt-6-luna | 2026-10-01 13:21 | yes | 6 | 17 s |
| Comparative | Muse Glimmer 30B | meta/muse-glimmer-30b via togetherai | 2026-10-01 11:38 | yes | 11 | 32 s |
| Budget constrained | Claude Haiku 4.5 | claude-haiku-4-5-20251001 | 2026-10-01 13:41 | no | 0 | 5 s |
| Budget constrained | GPT-5.4 mini | gpt-5.4-mini-2026-03-17 | 2026-10-01 11:30 | no | 0 | 4 s |
| Budget constrained | Gemini 3.5 Flash | gemini-3.5-flash | 2026-10-01 10:38 | yes | 16 | 25 s |
| Budget constrained | Perplexity Sonar | sonar | 2026-10-01 10:20 | yes | 19 | 2 s |
| Budget constrained | Grok 4.1 Fast | spacexai/grok-4.1-fast-non-reasoning via vertex | 2026-10-01 12:30 | yes | 18 | 5 s |
| Budget constrained | Mistral Small | mistral/mistral-small via mistral | 2026-10-01 10:31 | yes | 5 | 3 s |
| Budget constrained | DeepSeek V4 Flash | deepseek/deepseek-v4-flash via deepinfra | 2026-10-01 10:41 | yes | 22 | 26 s |
| Budget constrained | Llama 4 Maverick | meta/llama-4-maverick via bedrock | 2026-10-01 09:22 | yes | 5 | 1 s |
| Budget constrained | Qwen 3.7 Flash | alibaba/qwen3.7-flash via alibaba | 2026-10-01 10:20 | no | 0 | 27 s |
| Budget constrained | Kimi K2 | moonshotai/kimi-k2 via novita | 2026-10-01 11:30 | yes | 15 | 17 s |
| Budget constrained | GLM 4.7 FlashX | zai/glm-4.7-flashx via zai | 2026-10-01 12:25 | yes | 14 | 83 s |
| Budget constrained | MiniMax M2.5 | minimax/minimax-m2.5 via minimax | 2026-10-01 08:12 | no | 0 | 7 s |
| Budget constrained | GPT-6 Luna | gpt-6-luna | 2026-10-01 09:05 | yes | 2 | 15 s |
| Budget constrained | Muse Glimmer 30B | meta/muse-glimmer-30b via togetherai | 2026-10-01 13:22 | yes | 15 | 23 s |
| Scale constrained | Claude Haiku 4.5 | claude-haiku-4-5-20251001 | 2026-10-01 12:31 | no | 0 | 5 s |
| Scale constrained | GPT-5.4 mini | gpt-5.4-mini-2026-03-17 | 2026-10-01 13:07 | no | 0 | 7 s |
| Scale constrained | Gemini 3.5 Flash | gemini-3.5-flash | 2026-10-01 13:34 | yes | 7 | 22 s |
| Scale constrained | Perplexity Sonar | sonar | 2026-10-01 09:04 | yes | 20 | 5 s |
| Scale constrained | Grok 4.1 Fast | spacexai/grok-4.1-fast-non-reasoning via vertex | 2026-10-01 13:33 | no | 0 | 8 s |
| Scale constrained | Mistral Small | mistral/mistral-small via mistral | 2026-10-01 09:28 | no | 0 | 6 s |
| Scale constrained | DeepSeek V4 Flash | deepseek/deepseek-v4-flash via deepinfra | 2026-10-01 10:53 | yes | 20 | 36 s |
| Scale constrained | Llama 4 Maverick | meta/llama-4-maverick via bedrock | 2026-10-01 08:02 | yes | 5 | 3 s |
| Scale constrained | Qwen 3.7 Flash | alibaba/qwen3.7-flash via alibaba | 2026-10-01 12:02 | no | 0 | 46 s |
| Scale constrained | Kimi K2 | moonshotai/kimi-k2 via novita | 2026-10-01 13:23 | yes | 10 | 16 s |
| Scale constrained | GLM 4.7 FlashX | zai/glm-4.7-flashx via zai | 2026-10-01 10:00 | no | 0 | 12 s |
| Scale constrained | MiniMax M2.5 | minimax/minimax-m2.5 via minimax | 2026-10-01 10:31 | yes | 5 | 25 s |
| Scale constrained | GPT-6 Luna | gpt-6-luna | 2026-10-01 08:45 | yes | 3 | 18 s |
| Scale constrained | Muse Glimmer 30B | meta/muse-glimmer-30b via togetherai | 2026-10-01 12:03 | yes | 15 | 28 s |
| Negative framing | Claude Haiku 4.5 | claude-haiku-4-5-20251001 | 2026-10-01 10:05 | yes | 17 | 8 s |
| Negative framing | GPT-5.4 mini | gpt-5.4-mini-2026-03-17 | 2026-10-01 07:42 | yes | 6 | 6 s |
| Negative framing | Gemini 3.5 Flash | gemini-3.5-flash | 2026-10-01 12:53 | yes | 16 | 22 s |
| Negative framing | Perplexity Sonar | sonar | 2026-10-01 07:59 | yes | 17 | 4 s |
| Negative framing | Grok 4.1 Fast | spacexai/grok-4.1-fast-non-reasoning via vertex | 2026-10-01 12:58 | yes | 20 | 9 s |
| Negative framing | Mistral Small | mistral/mistral-small via mistral | 2026-10-01 11:51 | yes | 5 | 3 s |
| Negative framing | DeepSeek V4 Flash | deepseek/deepseek-v4-flash via deepinfra | 2026-10-01 10:32 | yes | 24 | 29 s |
| Negative framing | Llama 4 Maverick | meta/llama-4-maverick via bedrock | 2026-10-01 12:13 | yes | 5 | 2 s |
| Negative framing | Qwen 3.7 Flash | alibaba/qwen3.7-flash via alibaba | 2026-10-01 11:55 | yes | 5 | 30 s |
| Negative framing | Kimi K2 | moonshotai/kimi-k2 via novita | 2026-10-01 12:10 | yes | 24 | 31 s |
| Negative framing | GLM 4.7 FlashX | zai/glm-4.7-flashx via zai | 2026-10-01 11:50 | yes | 25 | 240 s |
| Negative framing | MiniMax M2.5 | minimax/minimax-m2.5 via minimax | 2026-10-01 11:57 | yes | 20 | 29 s |
| Negative framing | GPT-6 Luna | gpt-6-luna | 2026-10-01 10:18 | yes | 4 | 17 s |
| Negative framing | Muse Glimmer 30B | meta/muse-glimmer-30b via togetherai | 2026-10-01 07:43 | yes | 13 | 30 s |
Normalization in this category
Every judgment call made between the raw labels and the numbers above, listed so it is visible and reversible.
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Category-scoped readings
AWS SageMaker Studio read as Amazon SageMaker
Databricks read as Databricks Mosaic AI
Databricks Machine Learning read as Databricks Mosaic AI
Databricks Machine Learning (Lakehouse AI) read as Databricks Mosaic AI
Domino read as Domino Data Lab
Google Cloud AI Platform / Vertex AI read as Google Vertex AI
KNIME read as KNIME Analytics Platform
Knime read as KNIME Analytics Platform
Palantir read as Palantir Foundry
Snowflake read as Snowflake Cortex AI
Snowflake ML read as Snowflake Cortex AI
Unresolved, counted raw
AWS SageMaker Autopilot
Airtable Automations
Anaconda AI Platform
AutoGluon
Azure Machine Learning Studio
Beam
Bubble
Claude for Business
Clay.com
Databricks Lakehouse Platform
DeepInfra
Google AutoML tables
Google Cloud AI
Google Cloud AI / Vertex AI
Google Cloud AI/ML Tools
Google’s Deep Learning VM Image
HubSpot's AI
Hugging Face AutoTrain / Hugging Face Hub
HuggingFace
Iterative.ai
Jasper
Kaggle Kernels
Kimola Cognitive
LLaMA
LangChain-based hosting
Local open-source (e.g., TensorFlow/PyTorch + Jupyter)
MATLAB-based ML tooling
Microsoft Azure Free Trial
Microsoft R Open
MindStudio
MindsDB
Nyckel
Paperspace Core
Python + scikit-learn
Qlik AutoML
QuickBooks AI
Roboflow
SageMaker Studio Lab
Simplismart’s MLOps platform
Stability AI
SuperAnnotate
Valohai
Vercel AI Stack
Vertex AI on Google Cloud
Weights & Biases (W&B) / W&B Weave
open-source tools + cheap cloud compute
Discontinued, still offered
No shut-down product was recommended here.