Loading tool details…
Compare listed plans and check current offer terms with DataRobot
The catalog lists this free option; confirm its current limits and eligibility with DataRobot
💡 Pro tip: Start with the free tier to test if DataRobot fits your workflow before upgrading to a paid plan.
per month
per month
This guide does not establish that DataRobot has an active coupon, education discount, or seasonal promotion. Confirm current offers on the vendor's website before paying.
If DataRobot's pricing doesn't fit your budget, consider these data & analytics alternatives:
Enterprise AI platform uniquely converging predictive machine learning and generative AI with autonomous agents, featuring air-gapped deployment, FedRAMP compliance, and the industry's only truly free enterprise AutoML through H2O-3 open source.
Starting at Free (Open Source)
✓ Free plan available
DataRobot is used to build, deploy, monitor, and govern AI and machine learning models at enterprise scale. It supports predictive use cases such as forecasting, classification, regression, anomaly detection, and time series analysis, as well as generative AI applications including RAG-powered assistants, document intelligence, and agentic workflows. Common industry applications include credit risk scoring in financial services, demand forecasting in retail, predictive maintenance in manufacturing, patient readmission prediction in healthcare, and automated underwriting in insurance.
No. DataRobot offers a no-code/low-code interface that lets analysts and business users build models through a guided UI with drag-and-drop data preparation, automated feature engineering, and visual model comparison. However, it also supports a full code-first experience with Python and R SDKs, hosted Jupyter notebooks, and a comprehensive REST API, making it equally suitable for experienced data scientists and ML engineers who prefer programmatic control over their workflows.
DataRobot provides tooling for building, evaluating, and governing generative AI applications, including support for retrieval-augmented generation (RAG), vector databases like Pinecone and Weaviate, agent workflows, and side-by-side comparison of LLM providers such as OpenAI, Anthropic, Google, and Cohere. Teams can build custom AI assistants with prompt management, evaluation harnesses for hallucination and quality metrics, and deploy them with the same governance, monitoring, and access controls used for predictive models.
DataRobot can be deployed as a managed SaaS, in a virtual private cloud, on-premises, or in hybrid and air-gapped environments. It integrates with major data platforms like Snowflake, Databricks, SAP, BigQuery, and all three major cloud providers (AWS, Azure, GCP) for both data access and model serving. This flexibility allows organizations with strict data residency, compliance, or security requirements to run the full platform within their own infrastructure while maintaining feature parity with the SaaS offering.
Cloud-native ML platforms like SageMaker, Azure ML, and Databricks are highly flexible toolkits that require more engineering to assemble end-to-end workflows. DataRobot is more opinionated and turnkey: it automates model selection, feature engineering, and deployment pipelines out of the box with minimal configuration. DataRobot also differentiates with stronger built-in governance (approval workflows, bias detection, compliance documentation), a unified experience for both predictive and generative AI, and deployment flexibility across any cloud or on-premises environment without vendor lock-in to a single cloud provider.
Start with the free tier and upgrade when you need more features
Get Started with DataRobot →Listed prices may change. Confirm current prices and offer terms with the vendor.