Data science & ML platforms · DataRobot, Inc.

DataRobot

Enterprise AutoML and generative AI platform for building, deploying, and governing predictive models.

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DataRobot is an enterprise machine learning platform centered on automation: its AutoML engine automatically tests and ranks many modeling approaches, and its generative AI tooling helps build, evaluate, and govern LLM-based applications alongside traditional predictive models. It includes MLOps features for monitoring deployed models, tracking drift, and managing governance/approval workflows, aimed at organizations that need to operationalize many models with limited data science headcount. DataRobot is available as a managed cloud service or as a self-managed deployment for regulated environments. No pricing figures are published; prospective customers must request a demo and quote directly from DataRobot.

At a glance

Vendor DataRobot, Inc.
Pricing model Quote only
Free tier No
Deployment Cloud, Self-hosted
Open source No
Best for Enterprises wanting to automate model building and governance across many use cases at scale.

Pricing

No published pricing; DataRobot requires a demo request and custom quote from sales.

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

Features

  • Automated machine learning (AutoML)
  • Generative AI application building and evaluation
  • Model deployment and monitoring (MLOps)
  • Governance and approval workflows
  • Model explainability and bias testing
  • Time series and demand forecasting
  • Self-managed and cloud deployment options

Integrations

Profile last reviewed September 21, 2026

Head to head

Alternatives

H2O.ai Open-source machine learning library and framework, paired with commercial enterprise AutoML and MLOps products. Dataiku Collaborative platform spanning data preparation and machine learning, used by both analysts and data scientists. Domino Data Lab Enterprise MLOps and data science platform for running, governing, and scaling model workflows across teams.

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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. Predictive analytics Analytics that uses historical data and statistical models to estimate what is likely to happen next. Time series forecasting Predicting future values of a metric using patterns found in its own historical sequence over time. AutoML (automated machine learning) Tooling that automates parts of building a machine learning model, such as feature selection, algorithm choice and hyperparameter tuning. Demand forecasting Estimating future customer demand for a product so inventory, production and staffing can be planned ahead. MLOps (machine learning operations) The set of practices for deploying, monitoring and maintaining machine learning models reliably in production, extending DevOps to ML systems.

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.