Data science & ML platforms · Domino Data Lab, Inc.

Domino Data Lab

Enterprise MLOps and data science platform for running, governing, and scaling model workflows across teams.

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Domino Data Lab provides a unified workspace where data scientists can run notebooks, IDEs, and jobs on managed compute (including GPUs) while enterprise IT retains centralized governance, reproducibility tracking, and cost controls. It is tool- and language-agnostic, letting teams bring their own frameworks (Python, R, Spark, and others) rather than locking them into a proprietary modeling language. Domino is offered as Domino Cloud (single-tenant SaaS), or as a self-managed deployment (Premium/Enterprise) installed in a customer's own VPC or on-premises environment, which matters for regulated industries such as finance and pharma. Pricing is not published and is quoted per organization based on deployment model and scale.

At a glance

Vendor Domino Data Lab, Inc.
Pricing model Quote only
Free tier No
Deployment Cloud, Self-hosted
Open source No
Best for Regulated enterprises needing centralized governance over many data scientists' compute and models.

Pricing

No published pricing; Domino sells subscriptions by quote across Domino Cloud, Premium, and Enterprise self-managed tiers.

Checked on the vendor's own page on September 21, 2026: no prices are published. Expect to be quoted.

Features

  • Managed compute for notebooks, IDEs, and jobs
  • Bring-your-own-framework model development
  • Centralized reproducibility and experiment tracking
  • Model deployment and monitoring
  • GPU and distributed compute support
  • Self-managed VPC/on-premises deployment option
  • Governance and cost controls for IT/admins

Integrations

Profile last reviewed September 21, 2026

Alternatives

DataRobot Enterprise AutoML and generative AI platform for building, deploying, and governing predictive models. Dataiku Collaborative platform spanning data preparation and machine learning, used by both analysts and data scientists. Amazon SageMaker AWS's fully managed service for building, training, and deploying machine learning models at cloud scale.

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Terms to know

Machine learning Building models that learn patterns from data to make predictions or decisions, rather than following hand-coded rules. Data pipeline The automated sequence of steps that moves data from source systems to a destination, transforming it along the way. Data governance The policies, roles and processes that determine how data is defined, owned, accessed and kept compliant. MLOps (machine learning operations) The set of practices for deploying, monitoring and maintaining machine learning models reliably in production, extending DevOps to ML systems. Reproducibility The ability of an independent researcher to obtain the same results using the original data, code and methods.

Related guides

How to choose a data science & ML platform The decisions that matter are who builds the models, how automated the modeling should be, and whether you want a cloud-native platform or a portable one.