Quote-based; Domino does not publish set prices. Tiers include Domino Cloud (single-tenant SaaS), Premium (self-managed VPC/on-prem), and Enterprise (self-managed, for large regulated orgs), all requiring a sales quote.
Verified
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Last updated
August 6, 2026
Founded
2013
EnterpriseAI
Domino Data Lab is an enterprise AI platform designed to help large, regulated organizations build, deploy, and govern AI and machine learning models at scale. Founded in 2013 and based in San Francisco, Domino organizes its platform around three core pillars: Model Factory, a development environment supporting statistical computing, coding assistants (Cursor, Jupyter, RStudio, VS Code), and agentic AI workflows; App Hub, for deploying AI applications (including Streamlit, Dash, and Shiny apps) to end users at scale; and Governance Center, which provides policy enforcement, audit trails, lineage tracking, and cost visibility across the AI lifecycle. The platform runs on Kubernetes-based infrastructure and can be consumed as a single-tenant SaaS product or self-managed in a customer's VPC or on-premises environment across AWS, Azure, and GCP. Domino is used heavily by regulated industries such as life sciences, financial services, and the public sector, where reproducibility, compliance (ISO 27001, SOC 2, HIPAA, GDPR, 21 CFR Part 11 support), and model risk management are priorities. Customers include organizations like Bayer and Moody's, and investors include Sequoia Capital, Coatue Management, NVIDIA, and Snowflake.
Key Features
Model Factory — Development environment for building and fine-tuning models, supporting Jupyter, RStudio, VS Code, Cursor, distributed training, and hyperparameter optimization.
App Hub — Deploys and auto-scales AI applications, including apps built with Streamlit, Dash, or Shiny, to end users.
Governance Center — Policy enforcement, audit trails, lineage tracking, and reproducibility controls across the AI lifecycle.
AI Infrastructure — Kubernetes-based on-demand compute from CPUs to GPU clusters, with hybrid orchestration and cost controls.
FinOps — Cost optimization tooling including spot-instance management, budget enforcement, and usage reporting.
Flexible Deployment — Available as single-tenant SaaS (Domino Cloud) or self-managed in a customer's VPC or on-premises across AWS, Azure, and GCP.
Pros & Cons
Pros
Unified platform spanning model development, deployment, and governance, reducing tool sprawl for enterprise data science teams
Strong compliance posture (ISO 27001, SOC 2, HIPAA, GDPR, 21 CFR Part 11 support) suited to regulated industries
Flexible deployment as single-tenant SaaS or self-managed VPC/on-prem across AWS, Azure, and GCP
Supports popular developer tools out of the box, including Jupyter, RStudio, VS Code, and Cursor
Built-in FinOps tooling for compute cost visibility and budget enforcement on GPU/CPU workloads
Cons
Pricing is not publicly disclosed, requiring a sales conversation before enterprises can evaluate cost
Platform breadth and governance tooling likely add setup complexity compared to lightweight, single-purpose ML tools
Primarily built for large enterprises, so smaller teams or individual data scientists may find it more than they need