Overview
DataRobot is a unified AI platform that automates building, deploying, and monitoring machine learning and generative AI models at enterprise scale. It provides AutoML, MLOps, and GenAI tooling so data scientists, analysts, and business teams can create production-ready AI applications faster and with stronger governance.
Pricing
Usage-Based / Standard Plans
- Public benchmarks indicate a standard cloud plan starting around 99 USD per month for up to 100,000 predictions, with usage-based overages.
- These entry plans include AutoML, core data prep, and basic integrations suitable for smaller workloads or pilots.
Enterprise Licensing
- Most DataRobot deployments use enterprise contracts with annual commitments; vendors and customers often report six- to seven-figure deals per year.
- Pricing is driven by number of users, active deployments, prediction volumes, and environment (SaaS, VPC, on-prem).
Deployment & Services
- DataRobot offers Managed SaaS, VPC-hosted, and self-managed installations, plus professional services for use case design, onboarding, and model governance.research.
- Services help enterprises build bespoke predictive and generative AI applications on top of the platform.
Key Features
- AutoML & AutoTS – Automated modeling for regression, classification, and time-series forecasting, training hundreds of models in parallel.
- GenAI & LLM Ops – Tools for text generation, vector databases, LLM integration, and guard libraries for GenAI application moderation.
- MLOps & monitoring – Deployment management, drift detection, champion/challenger, and real-time performance monitoring across thousands of models.
- Governance & compliance – Guard Library, explainability, audit trails, approvals, and policy controls for regulated environments.
- Flexible deployment – Support for SaaS, private cloud, on-prem, and edge deployments, with integrations for Snowflake, Azure, AWS, GCP, and more.
Best Use Cases
- Enterprise predictive analytics – Demand forecasting, churn prediction, pricing, risk scoring, and resource planning.
- Financial services & insurance – Credit risk, fraud detection, underwriting, and claims analytics with strong governance.
- Healthcare & life sciences – Patient risk models, diagnostics, and operational forecasting under strict compliance requirements.
- Retail & manufacturing – Supply chain, inventory optimization, marketing response modeling, and quality prediction.
- GenAI business apps – Enterprise-grade text generation, summarization, and retrieval-augmented applications with moderation.
Pros
- ✅ End-to-end enterprise platform – Covers AutoML, MLOps, and GenAI in one stack, reducing tool sprawl.
- ✅ Scalable & performant – Handles datasets up to ~100 GB per table, thousands of models, and tens of billions of predictions.
- ✅ Strong governance & security – ISO 27001, SOC 2 Type II, HIPAA, encryption, MFA, and rich governance features
- ✅ Democratization of AI – Designed so both expert data scientists and less technical analysts can build and deploy models.
Cons
- ❌ High enterprise cost – Typical contracts are expensive compared with lighter-weight AutoML or open-source stacks.
- ❌ Complexity & learning curve – Full value requires process maturity, data engineering, and MLOps expertise.
- ❌ Better suited to large orgs – Smaller teams may find the platform overkill relative to their scale and budget.
Official Website
DataRobot – Official website and AI Platform: https://www.datarobot.com
Release Date: 2012
Last Updated: December 2025