Databricks Notebooks vs Domino Model Monitor
| Databricks Notebooks | Domino Model Monitor | |
|---|---|---|
| Free plan | Yes | |
| Paid from | Free | |
| Platforms | Web | api, self-hosted, Web |
| Model registry | Yes | Yes |
| Free plan | No | No |
| Deployment mode | both | |
| Autoscaling | Yes | |
| GPU accelerators | Yes | |
| Private deployment | Yes | |
| Supported model formats | MLflow, Python, scikit-learn, XGBoost, PyTorch, Hugging Face Transformers, custom code | |
| Batch inference | Yes | |
| Natural-language analysis | Yes | |
| AI explanations | Yes | |
| Forecasting | Yes | |
| Root-cause analysis | Yes | |
| Deployment options | cloud | hybrid |
| Storage model | both | |
| SQL analytics | Yes | |
| ACID transactions | Yes | |
| Table format support | both | |
| Streaming ingestion | Yes | |
| Governance catalog | Yes | |
| Automated discovery | Yes | |
| Column-level lineage | Yes | |
| Impact analysis | Yes | |
| Pipeline lineage | Yes | |
| Report lineage | Yes | |
| Deployment | hybrid | |
| Data scope | tables | |
| Dataset branching | Yes | |
| Point-in-time rollback | Yes | |
| Snapshot granularity | table | |
| Storage backend | vendor_hosted | |
| Deployment model | cloud | |
| Pricing model | usage_based | |
| Separate storage and compute | Yes | |
| Data lake support | Yes | |
| Experiment tracking | Yes | Yes |
| Pipeline orchestration | Yes | Yes |
| Model serving | Yes | Yes |
| Data versioning | Yes | Yes |
| Managed compute | Yes | Yes |
| Hybrid search | Yes | |
| Metadata filtering | Yes | |
| Serverless scaling | Yes | |
| Maximum dimensions | 4,096 dimensions | |
| Drift monitoring | Yes | |
| Model performance metrics | Yes | |
| Data quality checks | Yes | |
| Bias monitoring | Yes |
Listed together in Best ML Experiment Tracking SoftwareBest Model Registry SoftwareBest MLOps Platforms