Domino Data Lab
Domino helps enterprises operationalize AI through unified model lifecycle management.
Domino Data Lab provides an enterprise MLOps platform that unifies data science teams, tools, and infrastructure to industrialize AI at scale. It replaces fragmented workflows with a seamless lifecycle management system—from data exploration through model deployment, monitoring, and governance. The platform serves highly regulated enterprises (20% of Fortune 100) with open-source tool support, automatic versioning, and flexible deployment options. What differentiates Domino is its focus on organizational discipline and reproducibility rather than individual data scientist productivity.
Problem solved
Data science teams waste months navigating siloed tools, inconsistent environments, and broken workflows, making it impossible to reliably deploy and monitor models at enterprise scale.
Target customer
Enterprise organizations in regulated industries (finance, healthcare, manufacturing, defense) with mature data science teams needing to scale AI operations across 50-10,000+ employees.
Founders
N
Nick Elprin
CEO & Co-Founder
MS and BA in Computer Science from Harvard University; previously Quantitative Researcher at Bridgewater Associates.
C
Christopher Yang
CTO & Co-Founder
Co-founder at Domino Data Lab; background at Bridgewater Associates.
M
Matthew Granade
Co-Founder
Co-founder at Domino Data Lab; background at Bridgewater Associates.
Funding history
Seed
Unknown
March 27, 2015
Led by Unknown
· Unknown
Series F
$100M
October 5, 2021
Led by Coatue Management, Highland Capital Partners, Sequoia Capital
· Existing investors
Series F
Undisclosed
August 21, 2025
Led by The Private Shares Fund, Coatue, Great Hill Partners, Highland Europe
· Existing investors
Total raised:
$224M
Industries
Pricing
Tiered feature-based model with two deployment options: Domino Cloud (fully-managed SaaS with automated deployment and monitoring) and Self-hosted (on customer infrastructure with Kubernetes native support). Per-seat pricing not publicly disclosed; enterprise custom pricing based on usage and features.
Notable customers
Moody's, Bayer, U.S. Navy, Allstate, Dell, Tesla, Zurich
Integrations
NVIDIA AI Enterprise, Jupyter, RStudio, SAS, Anaconda, MATLAB, Apache Spark, Ray, Dask, MPI
Website
Competitors
Databricks
Focuses on data engineering and analytics workflows; Domino emphasizes end-to-end model lifecycle and governance for data science teams.
DataRobot
Emphasizes automated machine learning; Domino focuses on enabling disciplined, reproducible processes across teams with open-source tool flexibility.
Altair
Broader analytics platform; Domino specializes in MLOps and model operationalization at enterprise scale.
Why this matters: Domino has achieved rare 'Visionary' status in the Gartner Magic Quadrant while commanding 20% of Fortune 100 customers and raising $224M in a competitive MLOps market, signaling strong product-market fit with enterprises prioritizing model governance and AI industrialization. Recent funding activity (2025) and 26% YoY revenue growth ($22.6M in 2024) demonstrate sustained momentum in operationalizing AI at scale.
Best for: Fortune 500 and highly regulated enterprises that need to scale data science operations, ensure model governance, and reduce time-to-production for AI initiatives across distributed teams.
Use cases
Accelerating Model Deployment in Financial Services
Moody's reduced model deployment time by 50% and compressed a nine-month project to four months using Domino's streamlined workflow and model monitoring capabilities. Teams can now iterate faster while maintaining regulatory compliance and audit trails required in banking.
Defense AI Operationalization
The U.S. Navy reduced AI model deployment time by 75% for mine detection systems using Domino's reproducible environments and governance framework. This enabled faster iteration on critical defense applications while improving stakeholder trust through transparent, auditable processes.
Enterprise-Scale Model Management
Large enterprises manage hundreds of models across multiple teams with consistent versioning, monitoring, and remediation workflows. Domino provides centralized governance while allowing teams to use their preferred tools—eliminating the 'pick a standard tool or go rogue' dilemma.
Alternatives
Databricks
Choose Databricks if you need a unified data and ML platform with strong data engineering capabilities; choose Domino if model governance and reproducibility across dispersed teams is the priority.
AWS SageMaker
SageMaker is cloud-native and tightly integrated with AWS; Domino is cloud-agnostic and emphasizes reproducibility and governance regardless of deployment environment.
Kubeflow
Kubeflow is open-source and requires significant infrastructure expertise; Domino is a managed platform requiring less ops overhead and providing enterprise support.
FAQ
What does Domino Data Lab do? +
Domino Data Lab is an MLOps platform that unifies data science teams and tools under a single platform for model development, deployment, monitoring, and governance. It replaces fragmented workflows with automated versioning, reproducible environments, and enterprise-grade model lifecycle management. The platform supports open-source tools like Jupyter, RStudio, and distributed computing frameworks.
How much does Domino Data Lab cost? +
Domino uses custom enterprise pricing based on usage, features, and deployment option (Domino Cloud SaaS vs. Self-hosted). Per-seat pricing is not publicly disclosed. Contact their sales team for a custom quote.
What are alternatives to Domino Data Lab? +
Key alternatives include Databricks (unified data and ML platform), DataRobot (automated ML focus), AWS SageMaker (cloud-native ML platform), and Kubeflow (open-source Kubernetes-based MLOps). Each prioritizes different aspects of the ML lifecycle—Domino emphasizes governance and reproducibility for enterprise teams.
Who uses Domino Data Lab? +
Target customers are enterprise organizations in regulated industries (finance, healthcare, defense, manufacturing) with mature data science teams of 50+ people. Notable named customers include Moody's, Bayer, the U.S. Navy, Allstate, Dell, Tesla, and Zurich. The platform serves 20% of the Fortune 100.
How does Domino compare to Databricks? +
Databricks excels at unifying data engineering and ML with a strong lakehouse foundation; Domino specializes in model operationalization and governance for data science teams. Databricks is better for organizations prioritizing data infrastructure; Domino is better for those needing reproducible, auditable model workflows across teams.
Tags
MLOps
model governance
data science platforms
reproducibility
model deployment
enterprise AI
model monitoring