DataRobot - 2025 Pricing, Features, Reviews & Alternatives

DataRobot is an automated machine learning platform enabling businesses to build and deploy machine learning models and create advanced AI applications with AI and ML experts. The platform captures best practices and safeguards to accelerate and scale new and existing data science capabilities. Additionally, DataRobot provides businesses with targeted AI and ML training and services through a dedicated team of AI architects, business consultants, and systems architects.

DataRobot contains an extensive library of powerful, open-source machine learning algorithms and techniques. These are tested to ensure the best model is delivered on a case-by-case basis by building and ranking multiple models for each AI use case, recommending the best fit to deploy. Once in place, DataRobot analyzes how data was processed, what features were engineered, and which algorithms were used as well as continuously monitoring service health and accuracy.

DataRobot uses an automated time series model to predict future values of a data series based on historical data and trends derived from backtesting. This provides users with improved forecasting accuracy for product demand, staffing, inventory, and more. The MLOps feature automates the deployment of models from any cloud-based or on-premise ML infrastructure such as Kubernetes and Spark. The software monitors deployed ML apps for performance issues including data drift analysis and model-specific metrics, as well as enabling continuous optimization, governance, and updates, providing users with the data required for maintaining strong governance practices.

DataRobot’s Paxata feature visually and interactively explores, combines, and shapes diverse data sets for Enterprise AI, turning them into data ready for ML and AI apps. This enables both novice and expert users to explore and enrich diverse data. Embedded algorithms provide users with AI assistance throughout data preparation activities such as data profiling, clustering, and cleaning, reducing the time it takes to prepare and shape the data.