3 categories · RankedDatabricks
Standing by category
| Category | Function | Share | Rank | Negative rate | Labels | Quadrant |
|---|---|---|---|---|---|---|
| ML platforms | Data platform | 12% | 2 of 99 | 10% | 30 | accepted challenger |
| Data warehouses | Data platform | 0% | 8 of 41 | 30% | 37 | criticized challenger |
| ETL and ELT | Data platform | 0% | 55 of 66 | 50% | 6 | under 10 labels · led by Airbyte at 32% |
Movement
By model
| Model | First choice | Alternative | Mention | Negative | Labels |
|---|---|---|---|---|---|
| Claude Haiku 4.5 | 0 | 0 | 4 | 1 | 5 |
| GPT-5.4 mini | 2 | 1 | 1 | 1 | 5 |
| Gemini 3.5 Flash | 1 | 5 | 0 | 2 | 8 |
| Perplexity Sonar | 0 | 3 | 0 | 0 | 3 |
| Grok 4.1 Fast | 0 | 3 | 4 | 4 | 11 |
| Mistral Small | 1 | 1 | 2 | 1 | 5 |
| DeepSeek V4 Flash | 1 | 2 | 3 | 4 | 10 |
| Llama 4 Maverick | 0 | 1 | 4 | 0 | 5 |
| Qwen 3.7 Flash | 1 | 2 | 2 | 2 | 7 |
| Kimi K2 | 0 | 3 | 0 | 2 | 5 |
| GLM 4.7 FlashX | 0 | 4 | 1 | 0 | 5 |
| MiniMax M2.5 | 1 | 3 | 0 | 0 | 4 |
By framing
What the models said for it
Verbatim evidence the judge attached to positive labels.
“I'd recommend starting with either Databricks or Azure Machine Learning” MiniMax M2.5 · ML platforms · direct prompt · first choice
“Best overall "sweet spot": Databricks (with MLflow) — my top recommendation” DeepSeek V4 Flash · ML platforms · paraphrase prompt · first choice
“The heavyweight champion of data engineering, machine learning, and AI.” Gemini 3.5 Flash · Data warehouses · comparative prompt · first choice
“I'd usually recommend Databricks as the default MLOps platform” GPT-5.4 mini · ML platforms · paraphrase prompt · first choice
And against it
Verbatim evidence attached to negative labels. A warning on a product with few labels is a warning; on a product with many, it is one voice among them.
“only if your roadmap is heavily ML/AI-driven ... but DBU pricing is unpredictable, and it's the most operationally expensive of the four” DeepSeek V4 Flash · Data warehouses · paraphrase prompt · soft negative
“Platforms with aggressive model deprecation policies (like some Azure Databricks and OpenAI services) can disrupt your operations” Kimi K2 · ML platforms · negative prompt · soft negative
“Billing/support friction (e.g., account suspensions over tiny invoices, slow resolution per community forums; risk score 49/100)” Grok 4.1 Fast · Data warehouses · negative prompt · soft negative
“Databricks introduces unnecessary engineering complexity and a steeper learning curve than a lean team requires” Gemini 3.5 Flash · Data warehouses · direct prompt · soft negative
Named alongside
What carried it into the answer
Citations exist only for the models that return a source list, four of the twelve in this edition, so these counts come from 155 of the 199 answers that named Databricks and are not a share of its labels.
Domains cited
308 of the 308 domain citations in answers naming Databricks came from somebody else's page.
Pages cited
Pages are listed as the models cited them.
Names read as Databricks
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