Sold by: Dataiku
Deployed on AWS
Accelerate Enterprise AI with Dataiku on AWS
4.4
Overview
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Dataiku 3-Minute Demo
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Product video
Dataiku is The Universal AI Platform™, empowering teams to deliver AI and analytics projects faster - all within a secure, collaborative, and governed environment.
- Data Scientists use familiar tools to focus on high-impact work, with automation and streamlined collaboration.
- Business Analysts get faster insights with intuitive data prep and accessible machine learning.
- Data Teams scale projects with built-in governance and transparency.
Built for AWS
- Connect securely to all data sources, including Amazon S3, Amazon Redshift, and Amazon RDS.
- Scale data and ML processing with Dataiku elastic compute powered by Amazon EKS for Python, R, Spark, and more.
- Accelerate AI development with pre-built workflows integrating AWS AI services, such as Amazon SageMaker and Amazon Comprehend.
- Distributed creation of advanced analytics through its visual platform, fostering greater collaboration between technical and non-technical teams.
- Leverage the Dataiku LLM Mesh to connect to Amazon Bedrock for Chat, RAG, and Agentic workflows.
AI at Scale, Supported Every Step
With expert services and a robust learning platform, Dataiku helps organizations of any size adopt AI at scale - quickly and confidently.
Highlights
- Take full advantage of your investment in the AWS platform with Dataiku's unique push down to Amazon's storage and compute.
- Empower more users to clean and enrich data, build advanced data pipelines and machine learning models in a visual interface.
- Accelerate deployment on AWS, leveraging Sagemaker and Bedrock, with a fully managed service (SaaS) hosted and managed by Dataiku.
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Financing for AWS Marketplace purchases
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. This entitles you to a specified quantity of use for the contract duration. If you choose not to renew or replace your contract before it ends, access to these entitlements will expire.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/12 months |
|---|---|---|
Dataiku | Contact us for pricing | $1.00 |
Vendor refund policy
All fees are non-cancellable and non-refundable except as required by law.
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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.
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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.
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By Databricks, Inc.
By Domino Data Lab
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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
AWS Service Integration
Secure connectivity to Amazon S3, Amazon Redshift, and Amazon RDS with push-down computation capabilities
Elastic Compute Scaling
Distributed processing powered by Amazon EKS supporting Python, R, Spark, and other frameworks for data and ML workloads
Pre-built AI Workflows
Integration with AWS AI services including Amazon SageMaker and Amazon Comprehend for accelerated AI development
Large Language Model Integration
LLM Mesh connectivity to Amazon Bedrock enabling Chat, RAG, and Agentic workflow capabilities
Visual Development Interface
Low-code visual platform for data preparation, pipeline creation, and machine learning model development accessible to both technical and non-technical users
Lakehouse Architecture
Built on a lakehouse foundation providing unified data storage and governance across data engineering, analytics, BI, data science, and machine learning workloads
Open Source Integration
Constructed on open source data projects and open standards to maximize flexibility and interoperability across the data ecosystem
Data Intelligence Engine
Powered by a Data Intelligence Engine that enables organizational access to data and insights across diverse user roles and technical skill levels
Unified Data Platform
Consolidates data, analytics, and AI workloads on a single common platform running on Amazon S3, eliminating traditional data silos
Collaborative Capabilities
Provides native collaboration features enabling data teams to work together across the entire data and AI workflow
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.
Standard contract
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Customer reviews
Banking
Powerful Dataiku Integrations, Though I Haven’t Used It Yet
Reviewed on Apr 14, 2026
Review provided by G2
What do you like best about the product?
All the integrations you can add in Dataiku are really powerful.
What do you dislike about the product?
Im not a user but i have an interest in dataiku
What problems is the product solving and how is that benefiting you?
As a data scientist, I feel the next update in June 2026 would transform my job and make it much easier and more efficient, especially for doing PoCs.
Airlines/Aviation
Easy-to-Use Recipes Make Scenario Setup Simple
Reviewed on Apr 14, 2026
Review provided by G2
What do you like best about the product?
Having easy to use recipes with an easy and simple way to setup scenarios
What do you dislike about the product?
It’s not that I dislike this, but I want to have easier tool to use AI with step by step tutorials
What problems is the product solving and how is that benefiting you?
Everything is consolidated into one environment. I have the ability to do so much things in Dataiku
Henri S.
Simple Data Analysis That Saves Time
Reviewed on Apr 14, 2026
Review provided by G2
What do you like best about the product?
The simplicity to analyse data, results, and the gain of time compared to doing all that in python in a classic IDE
What do you dislike about the product?
Recently I did not like how difficult and long it was to add input files in managed folders. I could not upload subfolemders for exemple. Also multiple times when I uploaded large number of files, some were not uploaded.
What problems is the product solving and how is that benefiting you?
I work in data science and we do everything in dataiku
Xiaoguang D.
Flexible AI Platform with Stellar UI, Needs Better Visualization and Deployment Support
Reviewed on Apr 14, 2026
Review provided by G2
What do you like best about the product?
I think the user interface of Dataiku is very user-friendly. Even if you don't have a strong data science or data engineering background, you can still use it by drawing boxes, which makes it accessible for many people. I also like that you can customize your solutions by writing your own code to cater to specific business needs. Additionally, with its fast-paced development, Dataiku regularly updates and upgrades the system to include the latest AI features, which I find awesome. The graphical, no-code environment significantly reduces my development life cycle, saving at least 50% of my time. It also makes interaction with end users easy because they can access our development environment to see progress and give quick feedback.
What do you dislike about the product?
So first of all, I think I got some limitation that you to be honest with you, because let's say, if you want to display and visualize a large dataset, it always has some limitation. And, also, I find out the dashboard in built by the API is not super fancy and super user friendly. Comparing to Power BI or the other visualization tools like Tableau, I think that's something that you can improve as well. Other main pinpoint for us is about the deployment. Because, you need to link to the different development, the requirements, how to deploy our AI solution, particularly to another cloud form. For example, AWS Azure, I think that we need a little bit more support on this.
What problems is the product solving and how is that benefiting you?
Dataiku helps me build AI solutions like multi-agent systems, handling both test images and numerical data. It significantly reduces my development life cycle by 50% and enhances collaboration by allowing quick user feedback, leading to faster project iterations.
Aurelien B.
Flexible and Visual, But Could Improve Code Management
Reviewed on Apr 14, 2026
Review provided by G2
What do you like best about the product?
I like that Dataiku makes data analysis more visual and less painful. I appreciate the flexibility of the solutions available, such as the ability to host custom Python webapps, use Python filters, build custom pipelines, and create custom scenarios. The initial setup was super easy after doing the trainings.
What do you dislike about the product?
Webapp code management is challenging because it involves working with one big file, and the limited Python API calls are restrictive.
What problems is the product solving and how is that benefiting you?
I find Dataiku makes data analysis more visual and less painful.