DataRobot
Enterprise platform for building, running and governing AI agents. DataRobot unifies agentic, generative and predictive AI, deploys on-premise, hybrid, multi-cloud or at the edge, and ships built-in observability and governance for regulated industries.
What is DataRobot?
DataRobot is an enterprise AI platform built by DataRobot, Inc., a Delaware corporation headquartered in Boston and founded in 2012. Its trajectory tracks the market it serves: first automated machine learning, then MLOps as models began reaching production, and today what the company calls an agent workforce platform — a single environment in which large organisations build, operate and govern AI agents instead of running them as perpetual pilots.
The product is organised into six modules. Agentic AI covers agent construction and execution; Generative AI brings LLM playgrounds, embeddings, vector databases and retrieval; Predictive AI carries the AutoML heritage of forecasting and scoring; AI Governance tracks every asset and activity and produces automated audit documentation; AI Observability watches agent quality in real time; and AI Foundation supplies the underlying platform services. Two components are open source — Covalent, which orchestrates compute dynamically across edge, cloud and on-premise, and syftr, which searches for the right balance between accuracy, latency and cost.
DataRobot frames its offer around three audiences: teams that build agents, teams that operate them and teams that govern them. Deployment is deliberately unconstrained — multi-tenant SaaS, Single-Tenant SaaS on AWS, Azure or GCP, virtual private cloud, fully self-managed on-premise installations, and edge. The platform is model-agnostic, with third-party models from Anthropic, OpenAI, Google, Amazon, Meta, Mistral, Cohere, DeepSeek, NVIDIA, Stability AI and Qwen listed in its AI Policy, alongside integrations spanning Snowflake, Databricks, SAP, Salesforce, Slack, Microsoft Teams, LangChain, MLflow and more.
The customer base is enterprise and largely regulated: government, oil and gas, life sciences, financial services, manufacturing and insurance, with returns of USD 60 million, 200 million and 70 million published for three named accounts. Analyst recognition includes three consecutive years as a Leader in the Gartner Magic Quadrant for Data Science and Machine Learning Platforms, IDC MarketScapes for AI Governance and ML Operations, the Forbes Cloud 100 and Fortune's Future 50. Security credentials are ISO 27001, SOC 2 Type II and a HIPAA-capable Single-Tenant SaaS offering. A 14-day free trial gives full platform access, and a documented public API is available at docs.datarobot.com.
What it does
- Build enterprise AI agents from customisable blueprints and plug them into existing systems
- Deploy agents and models anywhere — on-premise, hybrid, multi-cloud or at the edge
- Monitor agent quality and behaviour in real time and step in before problems reach customers
- Govern the whole lifecycle with enforceable access controls, approvals and automated audit documentation
- Compare LLMs, embeddings and vector databases to pick the right components for a use case
- Balance accuracy, latency and cost across an AI pipeline with the open-source syftr project
- Authenticate agents and users, and control their access to data and APIs
When to use DataRobot / When not to
A quick filter to help you decide if DataRobot is the right fit.
When to use DataRobot
- Large enterprises in regulated sectors — financial services, insurance, life sciences, manufacturing, oil and gas, public sector
- Data science and machine learning teams that need to move models and agents from prototype into production
- MLOps, DevOps and platform engineers responsible for running AI workloads reliably at scale
- Risk, compliance and AI governance teams that must document and audit what their AI actually does
- Organisations with data-residency or sovereignty constraints that need on-premise, single-tenant or edge deployment
When not to use DataRobot
- Individuals and hobbyists — the platform is sold exclusively to enterprises
- Teams on a small budget or needing transparent pricing, since no rates are published anywhere on the site
- Buyers who want to purchase online, as every route runs through a demo request or a sales conversation
- Anyone looking for a mobile experience — there is no iOS or Android application
- Users after a ready-made chatbot rather than a platform on which to build one
How to use DataRobot
A typical end-to-end flow, from setup to results.
- Start the 14-day free trial, signing up with a Google or GitHub account
- Create a first agent from a customisable template
- Connect your data sources — Snowflake, SQL databases, Amazon S3, warehouses, lakes or on-premise systems
- Secure those connections with OAuth or username and password, and have an administrator govern the OAuth configurations
- Build vector databases, explore the LLM playgrounds and run automated model comparisons
- Use syftr to settle the trade-off between accuracy, latency and cost
- Push agents and models to production in a single click
- Watch the real-time dashboards for performance, accuracy, cost and behaviour
- Invite colleagues into the workspace and share use cases and datasets
- For an enterprise rollout, wire up SAML SSO, LDAP for self-managed installs and multi-factor authentication, then call the API with a bearer token
Pros & Cons
Pros
- Unusual deployment freedom: on-premise, sovereign, hybrid, multi-cloud and edge are all supported
- Building, running and governing agents live in one platform rather than three separate tools
- Serious enterprise credentials — ISO 27001, SOC 2 Type II and a HIPAA-capable single-tenant offering
- Model-agnostic, with Anthropic, OpenAI, Google, Meta, Mistral, Cohere, DeepSeek and NVIDIA models available
- The contract leaves ownership of data, models and prediction outputs with the customer
- Transparency on the supply chain: a public subprocessor list with locations, and standard contractual clauses published in the open
- A 14-day trial with full platform access and no contract, which is a cheap way to form an opinion
Cons
- No published pricing at all — the pricing URL simply redirects to the homepage
- No permanent free plan; access stops after the 14-day trial unless you subscribe
- Nothing can be bought self-service, so a sales cycle is unavoidable
- No mobile application on either iOS or Android
- No support email address is published; the only routes are the support portal and a contact form
- The privacy policy still shows a March 2022 revision date while the AI Policy is dated July 2026
- Documentation is available in English and Japanese only, and the estate is scattered across several subdomains
Pricing & Plans
There is no permanent free plan and no published price point. DataRobot offers a 14-day free trial granting full platform access with no contract and no commitment, after which commercial terms are set individually through an Order placed under the Master Subscription Agreement. The pricing URL on the site redirects to the homepage, and no amount, currency or tier appears anywhere on the public pages. Professional services are described in two public documents, one of them specific to the public sector, again without figures. Card payments, where they apply, are handled by Stripe.
- Free Trial — 14 days
- full platform access
- no contract and no commitment
- opened with a Google or GitHub account
- Commercial subscription — contracted through an Order under the Master Subscription Agreement
- with no published tiers or prices
- multi-tenant SaaS
- Single-Tenant SaaS on AWS
- Azure or GCP including the HIPAA-capable offering
- and Self-Managed on-premise
- Professional Services packages
- with a separate public-sector edition
- Japan — a dedicated Master Subscription Agreement with its own support
- availability
- information security
- data processing and AI policies
Data, GDPR & hosting
A consolidated view of how DataRobot handles your data.
GDPR overview
Compliance is claimed in writing. The Trust Center states that DataRobot complies with the GDPR, CCPA, CPRA, CPA, VCDPA and the EU AI Act, and concrete mechanisms back it up: a published Data Processing Policy, EU standard contractual clauses in all three configurations plus the UK equivalents and the UK IDTA addendum, a public subprocessor list, a Government Data Request Policy and a Schrems II transfer FAQ. European, Swiss and UK residents are told they may access, correct, delete, object to, restrict and port their data, withdraw consent and complain to a supervisory authority. Privacy questions go to privacy@datarobot.com or by post to the Boston office, marked for the attention of the Data Protection Officer. Two gaps remain: no Article 27 EU representative is named, and the privacy policy still carries a March 2022 revision date.
Who owns the data?
Section 11.3 of the Master Subscription Agreement is explicit: the customer keeps all rights, title and interest in its Customer Data, in the models it creates and in the prediction data it generates. DataRobot retains ownership only of the Solution itself, its documentation and the underlying technology. The privacy roles follow the same split — the customer is the data controller, DataRobot the processor acting on its instructions. Individuals who want to access, correct or delete personal data held inside a customer's workspace must therefore approach that customer rather than DataRobot, which has no direct relationship with them.
Reuse rights
Customers may reuse their own data, models and predictions freely: the agreement grants them those rights and asks no permission. What DataRobot takes for itself is narrower, and disclosed. On the SaaS edition it automatically collects User Metrics through its User Activity Monitor — technical logs, login frequency, number of models deployed, feature usage and engagement, plus clickstream and mouse tracking — and Metadata describing datasets, project types, accuracy metrics, run times and the blueprints executed. Metadata is always anonymised of personal data and Customer Data, and User Metrics are anonymised whenever they are used beyond the purposes set out in the privacy policy. Product development work removes all personal data and Customer Data first. On-premise deployments expose none of this unless the customer or its organisation supplies it. Card payments go straight to Stripe, and DataRobot states that it does not sell the personal data it collects.
Data retention & training
Hosting summary
Hosting depends on the edition. On multi-tenant SaaS, Amazon Web Services hosts the platform in the United States for customers located in the Americas and Asia-Pacific, and in Ireland for customers located anywhere else or in countries covered by a European Commission adequacy decision. Single-Tenant SaaS runs on AWS, Google Cloud or Microsoft Azure in a location the customer selects from the provider's options, with dozens of cloud regions available and data sovereignty as the stated purpose. Self-Managed deployments sit entirely on the customer's own infrastructure. Around all of this, the support and tooling subprocessors — Salesforce, SupportLogic, Box, Google and Amplitude — operate from the United States. Data is encrypted in transit and at rest, customers may bring their own certificate authorities, and the platform is DNSSEC-compatible. One caveat for anyone checking independently: the IP address the domain resolves to belongs to a Fastly anycast CDN node and says nothing about where the data actually lives.
Things to keep in mind
Risks and trade-offs to weigh before adopting DataRobot.
- Budget blindness: with no published pricing you cannot size the investment before entering a sales process, and comparison against alternatives becomes guesswork
- The 14-day trial is short for an enterprise platform — it is easy to run out of time before an honest evaluation is finished, and to decide on impression rather than evidence
- Delegating decisions to agents can dull the human judgement that used to make them; the governance tooling exists because that drift is real, but it only helps if someone actually reads the audit trail
- The privacy policy carries a March 2022 date while the AI Policy is dated July 2026 — do not assume the older document reflects current practice without asking
- No Article 27 EU representative is named, which matters for European customers dealing with a controller established in the United States
- Certain categories of sensitive personal data are excluded from SaaS deployments, and DataRobot may delete Customer Data or suspend access where the agreement's content rules are breached
- Japanese customers and partners fall under a separate Master Subscription Agreement and separate policies, so terms read on the main site may not be the ones that apply
Setup & Integrations
Technical difficulty
Getting started is easy: the 14-day trial opens with a Google or GitHub account, and the suggested path has a first agent running within three days from a template. Production is another matter. An enterprise rollout means wiring SAML SSO, LDAP for self-managed installs, multi-factor authentication and governed OAuth data connections, then linking warehouses, lakes and on-premise databases; on-premise and edge deployments add real infrastructure work. DataRobot names DevOps, IT and security engineers among its intended users, which tells you what the project actually requires. Documentation, DataRobot University and professional services are available to help.
Deployment
Integrations
Supported languages
Behind DataRobot
Fundraising
Social
Resources
All the official URLs gathered for verification and reference.
Frequently asked questions
Is there a free trial?
How much does DataRobot cost?
Where is my data hosted?
Which security certifications does DataRobot hold?
Who owns the data and the models?
Does DataRobot train its models on customer data?
Is there an API?
Can DataRobot run on our own infrastructure?
Is there a mobile app, and is there a minimum age?
Is the list of subprocessors public?
Should you pick DataRobot?
DataRobot has been through more market cycles than most of the tools it now competes with. Founded in 2012, it helped popularise automated machine learning, moved into MLOps as models started reaching production, and has since rebuilt itself around agentic AI. That history shows in the product: governance, observability and lifecycle tracking are not bolted on, they sit in the same platform as the agent builder.
Its strongest argument is deployment freedom. Very few vendors will run the same platform in multi-tenant SaaS, in a single-tenant cloud region of your choosing, inside your own datacentre and at the edge — and for a bank, a defence contractor or a pharmaceutical group, that constraint usually decides the shortlist before anything else. The supporting evidence is solid too: ISO 27001, SOC 2 Type II, a HIPAA-capable offering, a published subprocessor list with locations, standard contractual clauses in the open, and a contract that leaves ownership of data, models and predictions with the customer.
The reservations are commercial rather than technical. No price is published anywhere, there is no permanent free plan, and nothing can be bought without a conversation — which effectively rules out small teams and makes budgeting impossible before the first sales call. Support runs through a portal with no published email address, and the privacy policy still carries a March 2022 revision date while the AI Policy was refreshed in July 2026, an inconsistency worth raising.
For a large organisation that already knows it must industrialise and govern its AI, and that carries infrastructure or sovereignty constraints, DataRobot is a credible answer, and the 14-day trial with full platform access is the cheapest way to test it. For anyone smaller, the economics will settle the question before the technology gets a chance to.
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