Sold by: DataRobot
Deployed on AWS
DataRobot is the world's leading end-to-end platform for building, governing & scaling generative + predictive AI on AWS.
4.3
Overview
DataRobot Enterprise AI Suite delivers a unified experience to design, deploy, and govern AI-powered applications across the full lifecycle - from data prep and multi-modal model training to agentic orchestration and real-time monitoring. DataRobot now features a brand-new UI, a composable GenAI App Builder, and AI-Ready Data pipelines that slash time-to-value for LLM and classical ML workloads. Organizations leverage DataRobot to accelerate business outcomes while meeting stringent security and compliance requirements - fully optimized for AWS services and infrastructure.
DataRobot is also the partner of choice for SAP customers across industries, where it accelerates delivery of AI-powered solutions for a variety of use cases. DataRobot's AI templates and AI Platform enable customers to rapidly leverage their SAP business data to deliver meaningful AI apps that can be leveraged across lines of business. Whether it's generating high-quality forecasts, accurate predictions, or AI-driven recommendations, DataRobot's templates can be either pre-configured or fully customized to meet business value needs.
Highlights
- Composable AI Apps & Agents: Low-code builder for predictive, generative, and agentic workflows
- Built-in Governance & Observability: Secure, audit, & monitor every model, prompt, and workflow
- Any Deployment, One Platform: SaaS, Dedicated Managed AI Cloud, or self-managed in your VPC
Details
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Deployed on AWS
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AWS Marketplace now accepts line of credit payments through the PNC Vendor Finance program. This program is available to select AWS customers in the US, excluding NV, NC, ND, TN, & VT.
Pricing
Pricing is based on the duration and terms of your contract with the vendor, and additional usage. You pay upfront or in installments according to your contract terms with the vendor. This entitles you to a specified quantity of use for the contract duration. Usage-based pricing is in effect for overages or additional usage not covered in the contract. These charges are applied on top of the contract price. If you choose not to renew or replace your contract before the contract end date, access to your entitlements will expire.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/12 months |
|---|---|---|
DataRobot AI Platform - Private Offers Only - Contact Us | Contact your DR Account Manager or aws@datarobot.com for private offer | $0.01 |
Dimension | Cost/unit |
|---|---|
Additional usage as defined in private offer contract | $0.01 |
Vendor refund policy
No refunds accepted
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Delivery details
Software as a Service (SaaS)
SaaS delivers cloud-based software applications directly to customers over the internet. You can access these applications through a subscription model. You will pay recurring monthly usage fees through your AWS bill, while AWS handles deployment and infrastructure management, ensuring scalability, reliability, and seamless integration with other AWS services.
Resources
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Support
Vendor support
email and telephone support available support@datarobot.com
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
Updated weekly
By DataRobot
By Domino Data Lab
Sentiment is AI generated from actual customer reviews on AWS and G2
Functionality
Ease of use
Customer service
Cost effectiveness
Positive reviews
Mixed reviews
Negative reviews
AI generated from product descriptions
Low-Code AI Application Development
Composable builder for constructing predictive, generative, and agentic workflows without extensive manual coding
Model and Workflow Governance
Built-in capabilities to secure, audit, and monitor models, prompts, and workflows throughout their lifecycle
Multi-Modal Model Training
Support for training across multiple data types and model architectures for both LLM and classical ML workloads
Flexible Deployment Options
Support for SaaS, Dedicated Managed AI Cloud, or self-managed deployment within customer VPC
AI-Ready Data Pipelines
Automated data preparation and pipeline orchestration optimized for reducing time-to-value in AI projects
Self-Service Infrastructure Access
One-click, governed access to data, tools, and compute resources through a self-service portal with support for open-source tools including Jupyter, RStudio, SAS, Anaconda, MATLAB, and distributed compute frameworks like Spark, Ray, Dask, and MPI.
Centralized Knowledge Management
Central hub for AI operations and knowledge across the enterprise enabling reproducibility, reusability, and cross-functional collaboration with audit-ready platform capabilities.
Integrated MLOps Workflows
End-to-end model development, deployment, and monitoring capabilities within a unified platform with support for preferred tools and languages, including seamless integration with Amazon SageMaker.
Multi-Cloud and Hybrid Deployment
Support for deployment across public cloud, hybrid, and multi-cloud environments through Domino Nexus, enabling workload execution across any compute cluster in any cloud, region, or on-premises infrastructure.
Model Governance and Compliance
Turnkey model governance, monitoring, and remediation with robust controls for compliance, reproducibility tracking, and audit-ready processes designed for regulatory requirements including GxP processes.
AWS Data Source Integration
Secure connectivity to Amazon S3, Amazon Redshift, and Amazon RDS with push-down computation capabilities.
Elastic Compute Scaling
Distributed data and machine learning processing powered by Amazon EKS supporting Python, R, Spark, and additional frameworks.
AWS AI Service Integration
Pre-built workflows integrating AWS AI services including Amazon SageMaker and Amazon Comprehend for accelerated AI development.
Large Language Model Connectivity
LLM Mesh capability enabling connections to Amazon Bedrock for Chat, Retrieval-Augmented Generation (RAG), and Agentic workflows.
Visual Analytics and ML Interface
Low-code visual platform for data preparation, pipeline creation, and machine learning model development accessible to both technical and non-technical users.
Standard contract
No
No
No
Customer reviews
Atharva S.
DataRobot Streamlines the Full ML Lifecycle with Intuitive AutoML and Governance
Reviewed on Aug 04, 2026
Review provided by G2
What do you like best about the product?
What I like best about DataRobot is how it simplifies the end-to-end machine learning lifecycle, from data preparation and model development to deployment and monitoring. The platform's intuitive interface, strong AutoML capabilities, and built-in model governance make it easy to build and manage predictive models without excessive manual effort. I also appreciate its explainability features, collaboration tools, and seamless integration with existing data ecosystems. Overall, DataRobot accelerates AI development, improves model reliability, and enables teams to deliver machine learning solutions faster while maintaining transparency and operational efficiency.
What do you dislike about the product?
One area where DataRobot could improve is offering more flexibility for advanced users who want deeper control over model configuration and customization beyond the automated workflows. While the platform is powerful and easy to use, some enterprise features have a learning curve, and navigating complex projects can occasionally feel overwhelming. I'd also like to see richer visualization options, broader integrations with additional open-source tools, and more granular cost and resource monitoring. Overall, the experience has been very positive, but enhanced customization, expanded integrations, and improved observability would make DataRobot even more valuable for organizations deploying machine learning at scale.
What problems is the product solving and how is that benefiting you?
DataRobot solves the challenge of building, deploying, and managing machine learning models by automating much of the data science workflow, including data preparation, model selection, feature engineering, evaluation, deployment, and monitoring. Instead of spending significant time on repetitive manual processes, teams can quickly develop accurate predictive models while maintaining governance, explainability, and operational oversight. This has accelerated model development, improved collaboration between technical and business teams, reduced time to production, and enabled faster, data-driven decision-making with reliable and scalable AI solutions.
Accounting
Automated ML Made Easy for Fast Model Building and Deployment
Reviewed on Jul 29, 2026
Review provided by G2
What do you like best about the product?
What I like best about DataRobot is its automated machine learning capabilities, which make it easy to build, deploy, and manage predictive models quickly without needing deep technical expertise.
What do you dislike about the product?
What I dislike about DataRobot is that it can be quite expensive and may feel less flexible for highly customized modeling compared to open-source solutions.
What problems is the product solving and how is that benefiting you?
DataRobot solves the problem of complex and time-consuming model development by automating machine learning workflows, benefiting me with faster model deployment, reduced need for specialized expertise, and quicker data-driven decisions.
Brauny N.
Empowers Non-Data Scientists to Build Solid Models
Reviewed on Jul 28, 2026
Review provided by G2
What do you like best about the product?
One nice thing is that people who aren’t full-on data scientists can still build decent models, so it doesn’t all get bottlenecked on a single team.
What do you dislike about the product?
Honestly, the price is the biggest issue for me. It’s a premium platform, and the licensing costs add up quickly, which makes it a tough sell for smaller teams or smaller projects.
What problems is the product solving and how is that benefiting you?
It mainly solves the classic “we have data, but turning it into working models takes forever” problem. Before, building, tuning, and comparing models was slow and required serious data science muscle. DataRobot automates most of that, so we can go from a dataset to a solid model in a fraction of the time.
The biggest benefits are speed and reach. We can ship predictions faster, and people who aren’t hardcore data scientists can still build usable models, which means we’re not bottlenecked by one small team. Once models are live, the monitoring helps catch drift and accuracy drops, so we’re not flying blind in production and we know when it’s time to retrain.
Net effect: faster time to value on ML projects and more consistent models, without having to grow a huge data science team.
The biggest benefits are speed and reach. We can ship predictions faster, and people who aren’t hardcore data scientists can still build usable models, which means we’re not bottlenecked by one small team. Once models are live, the monitoring helps catch drift and accuracy drops, so we’re not flying blind in production and we know when it’s time to retrain.
Net effect: faster time to value on ML projects and more consistent models, without having to grow a huge data science team.
Computer Software
Streamlined AutoML with Quick Deployment
Reviewed on Jul 16, 2026
Review provided by G2
What do you like best about the product?
I love using DataRobot because of the automated leaderboard, which is incredible for training and comparing dozens of different models side-by-side with just a single click. The built-in feature impact tools are also fantastic, making it super easy to explain our model's predictions to business stakeholders. I find that the leaderboard saves us weeks of manual coding and hyperparameter tuning by running dozens of algorithms in parallel. The feature impact charts visually break down what drives our predictions, which helps us build instant trust with business partners. Our setup was incredibly fast with their SaaS deployment, allowing us to connect to our data warehouses and run our first models on day one.
What do you dislike about the product?
I find the interface can sometimes feel overly dense and cluttered, which makes it tough for new users like me to figure out exactly where to click when navigating the platform. Additionally, I feel that DataRobot's enterprise-heavy licensing structure is quite restrictive, making it cost-prohibitive for smaller teams or startups looking to experiment with AutoML.
What problems is the product solving and how is that benefiting you?
I use DataRobot to quickly build, deploy, and monitor predictive models, cutting weeks of manual coding. It eliminates bottlenecks in model building and auto-alerts us to prediction drifts, speeding up our workflow and reducing maintenance stress.
Javed Bux
Automated custom churn and demand models have reduced manual work but still need faster processing
Reviewed on Jul 02, 2026
Review provided by PeerSpot
What is our primary use case?
Our main use case for DataRobot involves predicting SKU across multiple applications and stores, as we have some SKU and unit measurement SKUs where we want to predict our requirements for each store.
Imagine we have a store with a manufacturing unit where suppliers provide spare parts such as RAM and hard disks. We are predicting how much time suppliers take to deliver our hardware such as hard disks. We examine the common supply time across different suppliers to make accurate predictions.
Regarding our main use case with DataRobot, we are predicting churn to improve customer retention by analyzing customer history. We are creating models based on our own data, and this is the primary use case for DataRobot in our organization, which helps us develop AI tools based on these models.
What is most valuable?
The best feature DataRobot offers is converting data to models, which is the most handy part of the tool we are mostly utilizing. We have not explored the other aspects as my team focuses specifically on this area.
The feature of converting data to models stands out for me primarily because it is integrated with our internal cloud. Whatever model we create can be used by the entire organization, allowing for seamless AI deployment via a one-click API. When using OpenAI, we utilize the API to call the models similarly within our organization.
DataRobot has a positive impact on our organization because creating models outside of it is very difficult. Previously, we needed more manpower to create models using coding in Python or C#, but now we can easily create models using DataRobot.
By using DataRobot, we save the work equivalent of almost four to five people who are experts in Python and AI, as we can do the same tasks more easily with this tool.
What needs improvement?
The necessary improvement for DataRobot is its high licensing cost.
We also need a robust data infrastructure. For API deployment, we require enhanced data systems, including procuring new servers for GPU support. Faster algorithms would be beneficial as the process can sometimes be very slow.
For how long have I used the solution?
We have been using DataRobot for about six months now.
What do I think about the stability of the solution?
DataRobot is stable so far.
What do I think about the scalability of the solution?
DataRobot's scalability has allowed us to reduce the number of employees needed for model creation. Previously, we had a full-fledged team, but now we only need a few people due to the tool's ready-to-use nature.
How are customer service and support?
The customer support from DataRobot is good.
How was the initial setup?
Before choosing DataRobot, no other options were evaluated as it was selected by higher management.
What about the implementation team?
DataRobot is deployed in my organization through a private cloud.
Our cloud provider is Dell, as we use our own internal cloud infrastructure.
What was our ROI?
We have seen a return on investment primarily through time saved.
What's my experience with pricing, setup cost, and licensing?
My experience with pricing, setup costs, and licensing has been good. It is a bit expensive but remains very effective.
What other advice do I have?
The accuracy and reliability of DataRobot's output are excellent. We consistently receive proper output with no issues.
DataRobot is very slow compared to creating models directly from code in Python or C#. Although DataRobot is a very handy tool that creates models perfectly, it definitely needs improvements regarding speed.
In terms of DataRobot's AI capabilities, its governance and security are perfect, with proper handling of security protocols and the SSO we use to connect being very effective.
My advice to others looking into using DataRobot is that it is perfect for those wanting to create their own models for specific use cases such as pricing, sales, and purchases. You can build separate models for individual needs, making it a lightweight solution that works effectively for simpler models. I would rate DataRobot a seven on a scale of one to ten.