DataRobot
AutoML pioneer turned enterprise agentic-AI and governance platform
Overview
DataRobot automates the machine-learning lifecycle — feature engineering, model training and selection, deployment and drift monitoring — and has pivoted toward agentic AI, LLM evaluation and AI governance. It's a three-time Gartner Magic Quadrant Leader aimed at data teams in large enterprises.
Pricing
Pricing shown for reference only. These figures reflect RECATOOLS research as of 13 Jul 2026 and may be out of date or incomplete. This is not financial or purchasing advice — always confirm the current price on the provider’s official website before making any decision.
- Full platform access
- Credits-limited usage
- No exports or sharing
- Community support only
- Usage per contract terms
- Governance + full support
- $50K-$500K+ typical budget
- Academic/gov/nonprofit discounts
What you can produce with DataRobot
- End-to-end AutoML (feature engineering to deploy to monitor)
- Model drift monitoring + automated retraining
- Agent Workforce Platform (build/run/govern AI agents)
- LLM evaluation + generative-AI tooling
- AI governance: RBAC, audit logging, GDPR/HIPAA
- 14-day credit-based free trial
- 3x Gartner Magic Quadrant Leader (DSML platforms)
ASEAN Perspective
DataRobot in Southeast Asia
ASEAN-region availability and pricing notes coming soon. Drop the editorial team a note via /contact/ if you can supply local context (Singapore/Malaysia/Indonesia/Thailand/Vietnam).
One of the original AutoML names, DataRobot has spent 2025-2026 repositioning around an 'Agent Workforce' platform (co-engineered with Nvidia) and LLM governance — a sensible pivot as pure AutoML commoditized. What still draws praise is the core: upload data, auto-engineer features, train and compare models, deploy, monitor drift and retrain, with enterprise-grade RBAC, audit logging and GDPR/HIPAA controls that make regulated teams comfortable putting agents in production. It's a three-time Gartner MQ Leader (4.6/5 across ~789 reviews). The catch is money and opacity: no public list pricing, a credits-based 14-day trial with exports and sharing disabled, and contracts reviewers openly call 'one of our most expensive vendor partnerships' — think $50K-$500K+ annual budgets aimed at $500M+ enterprises. Fit: data-science teams at large, well-funded organizations that want to move fast without hand-coding pipelines and need formal governance across many models. Overkill for small teams or one-off projects.
What people say
DataRobot lands well with analysts and enterprise reviewers, with the same asterisk that follows most platforms in this bracket: it works, and it's expensive.
The strongest external signal is Gartner. DataRobot was named a Leader in the 2026 Magic Quadrant for Data Science and Machine Learning Platforms for the third year running, evaluated on completeness of vision and ability to execute, and holds a 4.6/5 rating across roughly 789 Gartner Peer Insights reviews as of mid-2026. Reviewers there are blunt about what they like: 'still the best AutoML platform hands down,' citing the full loop of upload, feature-engineer, build, experiment, evaluate, deploy, monitor and retrain. Governance shows up repeatedly as a differentiator — 'strong enterprise-grade governance and monitoring' that gives teams confidence to push agentic AI into production, backed by role-based access control, audit logging and GDPR/HIPAA compliance.
The pivot is the story of the last year. DataRobot launched its Agent Workforce Platform on July 31, 2025, co-engineered with Nvidia, extending from classic AutoML into building, running and governing fleets of AI agents; it also acquired Agnostiq (and its Covalent distributed-compute stack) in early 2025 to support that direction. The generative-AI and LLM-evaluation features are described by reviewers as on par with leading platforms.
Cost is the consistent complaint. DataRobot publishes no list pricing and quotes custom per deployment, user count, compute and model volume. The 14-day free trial is real but credit-limited — run out and it drops to read-only, with no exports, no sharing and community-only support. Third-party pricing trackers (Vendr, Capterra) peg it squarely at large enterprises with $50K-$500K+ annual budgets, and users describe it as 'one of our most expensive vendor partnerships.'
Ownership and scale worth noting: DataRobot is still private, has raised over $1B across roughly 77 investors, and carries a wide valuation range in secondary markets — Tracxn cites figures as high as $6.3B while some 2025 secondary assessments put it far lower, reflecting the reset AutoML valuations took. For a mid-to-large data-science team that needs speed plus auditable governance across many models and, increasingly, agents, DataRobot remains a top-tier pick — provided the budget matches the ambition.
Summary of public user & expert reviews, compiled by RECATOOLS.
About this listing
This entry was compiled from publicly available data including DataRobot's official website, press releases, documentation, and reputable third-party publications. RECATOOLS is not affiliated with DataRobot unless explicitly stated.
Third-party AI tools update their pricing, features, availability, and policies frequently. Information here may be outdated by the time you read this — we make reasonable efforts to keep listings current, but cannot guarantee absolute accuracy.
For the latest details, please refer to DataRobot directly →
Spotted something out of date? Suggest an update →
More in Code & Dev Tools
