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Domino Model Monitor
San Francisco, California, United States
What it is
Domino Model Monitor evaluates production machine-learning models and alerts teams when data drift or model quality declines enough to require expert review. A central dashboard brings together model status, monitoring summaries, customized alerts, drift information, and quality results. Domino provides automated prediction-data pipelines and automatic monitoring-metric generation. For remediation, users can recreate a production model’s development environment, retrain it with production data, and redeploy a new version. Models built with Python, R, SAS, MATLAB, TensorFlow, or DataRobot can be registered. Its Elastic Monitoring Engine is designed to scale monitoring and ingest data from Amazon S3 and Hadoop-compatible storage, including Azure Blob Storage, Azure Data Lake, Google Cloud Storage, and HDFS. Deployment supports AWS, Azure, GCP, on-premises, and secure air-gapped environments. Domino lists performance metrics, data-quality checks, bias monitoring, a model registry, experiment tracking, pipeline orchestration, model serving, data versioning, and managed compute. Pricing is available on request, and the platform is positioned for large AI-driven enterprises and regulated, mission-critical work.
For and against
For
- Tracks data drift and model quality
- Central dashboard with configurable alerts
- Supports six listed model frameworks
- Self-managed and air-gapped deployment options
- Includes retraining and redeployment workflow
Against
- No free plan
- Pricing is available only on request
- Designed for large and regulated organisations
The verdict
Domino Model Monitor combines drift detection, quality monitoring, and remediation processes for production models. Its deployment and security options target organisations with demanding governance requirements rather than casual model use.
Who it suits
It suits large enterprises and regulated industries that need ongoing model-quality oversight, deployment controls, and retraining workflows. Teams using supported model frameworks or hybrid deployment environments may find the platform relevant.
Plans and prices
Domino Cloud
5 service accounts · 5 admin licenses · Unlimited consumer licenses · 1 deployment
Read 1 Oct 2026 · domino.ai
Enterprise
10 service accounts · 10 admin licenses · Unlimited consumer licenses · Up to 2 deployments
Read 1 Oct 2026 · domino.ai
Premium
5 service accounts · 5 admin licenses · Unlimited consumer licenses · Up to 2 deployments (1 prod, 1 non-prod)
Read 1 Oct 2026 · domino.ai
On the record
- Automated pipelines
- Domino provides simplified deployment tools, automated prediction data pipelines, and automatic generation of monitoring metrics.domino.ai · read 1 Oct 2026
- Central dashboard
- Users can view model status, monitoring summaries, customized alerts, data drift, and model quality from one dashboard.domino.ai · read 1 Oct 2026
- Deployment environments
- Domino supports self-managed deployment in a VPC on AWS, Azure, GCP, or on-premises, including secure air-gapped environments.domino.ai · read 1 Oct 2026
- Drift monitoring
- The monitoring platform tracks data drift and model quality degradation with integrated monitoring and alerts.domino.ai · read 1 Oct 2026
- Enterprise security
- Domino describes single-tenant isolation, secure development testing, vulnerability scanning, third-party penetration testing, and SIEM-integrated intrusion detection.domino.ai · read 1 Oct 2026
- Framework support
- Models built in Python, R, SAS, MATLAB, TensorFlow, or DataRobot can be registered for monitoring.domino.ai · read 1 Oct 2026
- Integrations
- Domino's partner ecosystem includes Anaconda, Snowflake, Palantir, RStudio, SageMaker, MathWorks, Stata, Collibra, DataRobot, H2O.ai, Okera, Teradata, Trifacta, and Pinnacle 21.domino.ai · read 1 Oct 2026
- Purpose
- Domino Model Monitor automates model evaluation and alerts when production models degrade enough to require expert review.domino.ai · read 1 Oct 2026
- Remediation
- Users can recreate a production model's development environment, retrain with production data, and redeploy a new version.domino.ai · read 1 Oct 2026
- Scale and storage
- Domino's Elastic Monitoring Engine is designed to scale monitoring capacity and ingest data from Amazon S3 and Hadoop-compatible file systems including Azure Blob Storage, Azure Data Lake, Google Cloud Storage, and HDFS.domino.ai · read 1 Oct 2026
- Security certifications
- Domino states that it is certified for ISO 27001, ISO 9001, SOC 2, HIPAA, and GDPR, and supports 21 CFR Part 11 and Annex 11 compliance.domino.ai · read 1 Oct 2026
- Snowflake monitoring
- Domino can automatically configure Snowflake-deployed models for drift and model quality analysis with prediction data capture pipelines and ground-truth datasets.domino.ai · read 1 Oct 2026
- Support
- The Cloud and Premium plans include premium support with a two-business-day non-urgent SLA, while Enterprise includes one-business-day non-urgent support.domino.ai · read 1 Oct 2026
- Target customers
- Domino positions its platform for large AI-driven enterprises and highly regulated industries performing mission-critical AI work.domino.ai · read 1 Oct 2026
