Sold by: Dataiku
Deployed on AWS
AWS Free Tier
Accelerate Enterprise AI with Dataiku on AWS
4.4
Overview
This trial version of Dataiku allows you to deploy into your AWS environment for prototyping, testing and evaluating the full extent of Dataiku capabilities.
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.
With Dataiku visual, end-to-end collaborative AI platform: - Data Scientists spend more time on high-impact AI projects, leveraging the languages and tools they already know, automating repetitive tasks and efficiently collaborating with stakeholders. - Business Analysts generate deeper intelligence, faster, thanks to comprehensive data access, smart data preparation and accessible machine learning. - Data Teams can deliver more projects and more value from analytics and AI all with built in transparency and governance. Dataiku and AWS innovate together to enable organizations of any size to deliver enterprise AI in a highly scalable environment. - Dataiku natively integrates with AWS Services and products to enable organizations of any size to deliver enterprise AI at scale. - Dataiku enables users to ingest and manipulate a wide variety of data including Athena, Redshift and more, from the AWS ecosystem and beyond. - Dataiku empowers analytic teams to extend data science collaboration through integrations with Amazon Sagemaker Get started today with Dataiku on AWS!
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 create 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.
Details
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Delivery method
Delivery option
64-bit (x86) Amazon Machine Image (AMI)
Latest version
Operating system
OtherLinux 9
Deployed on AWS
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Introducing multi-product solutions
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Features and programs
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 and entitlements for this product are managed through an external billing relationship between you and the vendor. You activate the product by supplying a license purchased outside of AWS Marketplace, while AWS provides the infrastructure required to launch the product. AWS Subscriptions have no end date and may be canceled any time. However, the cancellation won't affect the status of the external license.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Vendor refund policy
Refunds are not provided, but one can cancel at any time.
Legal
Vendor terms and conditions
Upon subscribing to this product, you must acknowledge and agree to the terms and conditions outlined in the vendor's End User License Agreement (EULA) .
Content disclaimer
Vendors are responsible for their product descriptions and other product content. AWS does not warrant that vendors' product descriptions or other product content are accurate, complete, reliable, current, or error-free.
Delivery details
64-bit (x86) Amazon Machine Image (AMI)
Amazon Machine Image (AMI)
An AMI is a virtual image that provides the information required to launch an instance. Amazon EC2 (Elastic Compute Cloud) instances are virtual servers on which you can run your applications and workloads, offering varying combinations of CPU, memory, storage, and networking resources. You can launch as many instances from as many different AMIs as you need.
Version release notes
Please read at https://doc.dataiku.com/dss/latest/release_notes/index.html
Additional details
Usage instructions
Browse to http(s)://INSTANCE_PUBLIC_ADDRESS/
You might need to wait few minutes that the instance starts and initializes.
You will have a first authentication to prove that you're the owner of the instance (with a basic access authentication):
- login = instance id
- password = empty
Then, you will have access to Dataiku DSS visual interface. Note that only Chrome and Firefox are supported.
Administrative (command-line) access can be obtained through ssh centos@INSTANCE_PUBLIC_ADDRESS. A standard installation of Dataiku DSS runs under linux user account "dataiku".
For additional information, or any issue, please see our resources and Q & A pages.
Resources
Support
Vendor support
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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Customer reviews
Louis K.
Dataiku Streamlines End-to-End AI at Scale with Intuitive, Collaborative Workflows
Reviewed on Jan 15, 2026
Review provided by G2
What do you like best about the product?
Dataiku streamlines building, training, and deploying AI models at scale, offering end-to-end automation, seamless orchestration of compute resources, and collaborative pipelines that make generative AI projects faster and more reliable. It unifies the entire data workflow from preparation and analysis to model deployment, into a single, collaborative platform that’s surprisingly intuitive for both data scientists and business users.
What do you dislike about the product?
Full capabilities can be overwhelming for new users, and scaling extremely large generative AI models sometimes requires complex customization.
What problems is the product solving and how is that benefiting you?
Dataiku solves the problem of fragmented AI workflows by unifying data preparation, model training, and deployment in a single platform. This benefits me by accelerating generative AI projects, reducing infrastructure complexity, and enabling seamless collaboration across data engineers, scientists, and business teams. It ensures models are production-ready faster while maintaining scalability and reliability.
Samantha L.
Centralized, Organized Data Platform with Powerful AutoML and Integrations
Reviewed on Jan 15, 2026
Review provided by G2
What do you like best about the product?
Dataiku demonstrates a satisfactory environment where data is centralized and organized
The program supports both coders and non coders, allowing them to use data in their different levels
Dataiku has a successful data lifecycle, something that collects, ingest, prepare and even analyze data
The program consists of an inbuilt Auto ML tools that speed u most of the operations
Dataiku has extensible APIs and plugins, all supporting success integrations
The program supports both coders and non coders, allowing them to use data in their different levels
Dataiku has a successful data lifecycle, something that collects, ingest, prepare and even analyze data
The program consists of an inbuilt Auto ML tools that speed u most of the operations
Dataiku has extensible APIs and plugins, all supporting success integrations
What do you dislike about the product?
Dataiku has challenges in cost management and estimating, where small companies fail to secure the app
The app demands extensive computer resources, something that amplifies the infrastructure costs
The app demands extensive computer resources, something that amplifies the infrastructure costs
What problems is the product solving and how is that benefiting you?
Dataiku ensure solid data collaboration, where analysts, engineers and even business players access data in a centralized environment
Most of complex data workflows are significantly supported by this app, ensuring that no manual code needed to conduct a specific task
The presence of machine learning and AI support s the effectiveness of data processing and analysis
The app accommodates both technical and non technical users due to it’s effectiveness and simplicity
Most of complex data workflows are significantly supported by this app, ensuring that no manual code needed to conduct a specific task
The presence of machine learning and AI support s the effectiveness of data processing and analysis
The app accommodates both technical and non technical users due to it’s effectiveness and simplicity
Kajal K.
End-to-End Data Science Platform That Makes Collaboration Easy
Reviewed on Jan 15, 2026
Review provided by G2
What do you like best about the product?
What I like best about Dataiku is its end-to-end data science and machine learning platform that brings data preparation, analysis, model building, and deployment into a single environment. The visual workflows combined with code-based options make it accessible for both technical and non-technical users. It also supports strong collaboration between data scientists, analysts, and business teams, which helps speed up model development and improve decision-making.
What do you dislike about the product?
While Dataiku is a powerful platform, it can feel complex for first-time users because of its extensive feature set. The initial setup and learning curve may take time, especially for non-technical users. In some cases, performance can slow down when handling very large datasets, and the pricing structure may not be ideal for smaller teams or limited use cases.
What problems is the product solving and how is that benefiting you?
It's solves the challenge of managing the entire data science and machine learning lifecycle in one platform. It brings together data preparation, analysis, model development, deployment, and monitoring, reducing the need for multiple disconnected tools. This benefits me by improving collaboration between teams, speeding up model development, and making it easier to turn data into actionable insights while maintaining consistency and governance across projects.
reviewer2784765
Low-code projects have empowered non-technical teams and now need better integration and visuals
Reviewed on Jan 05, 2026
Review from a verified AWS customer
What is our primary use case?
My main use case for Dataiku is data science and AI projects.
We used Dataiku for a demand forecasting project where the objective is to forecast the demand for each product for the next three months.
What is most valuable?
The best features Dataiku offers include the ability for users to use the node without having to code and the functionality related to low-code/no-code.
Dataiku has positively impacted my organization by allowing non-technical users to adapt a data science project and to maintain a part of a data science project.
What needs improvement?
I think a pain point related to Dataiku is the visualization, which is not straightforward, and the integration, which is also not straightforward for non-technical users.
To improve Dataiku, the company could enhance the capabilities related to integration and visualization.
For how long have I used the solution?
I have been using Dataiku for three years.
What do I think about the stability of the solution?
Dataiku is stable.
What do I think about the scalability of the solution?
Dataiku's scalability can be better.
How are customer service and support?
I have never tried Dataiku's customer support.
How would you rate customer service and support?
Which solution did I use previously and why did I switch?
Before, we used a solution that I cannot mention, but the change is more related to using a more straightforward solution for non-technical users.
Before choosing Dataiku, I evaluated KNIME.
What was our ROI?
I have not seen any specific outcomes or metrics such as time saved, reduced costs, or improved project delivery.
I have not seen a return on investment with Dataiku in terms of time saved, money saved, or fewer employees needed.
What's my experience with pricing, setup cost, and licensing?
I am not the person involved in the process regarding pricing, setup cost, and licensing.
What other advice do I have?
My advice to others looking into using Dataiku is to use it principally to help and support non-technical users.
Dataiku is deployed in my organization on a public cloud on Amazon Web Services.
Amazon Web Services is our cloud provider.
I am not the person involved in the process of determining whether we purchased Dataiku through the AWS Marketplace.
My review rating for Dataiku is 7.
Which deployment model are you using for this solution?
Public Cloud
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
Amazon Web Services (AWS)
reviewer2784765
Flow-based demand forecasting has improved collaboration but still needs better visualization options
Reviewed on Jan 04, 2026
Review from a verified AWS customer
What is our primary use case?
My main use case for Dataiku is for data science and AI projects. I use Dataiku for a demand forecasting use case where the objective is to predict the demand for each product for the next four months. Demand forecasting is the primary focus where I use Dataiku.
What is most valuable?
The best features Dataiku offers that help me with my demand forecasting and data science projects include having a complete overview of the flow directly from the flowchart, allowing me to observe all the steps in a single overview, and the ability to use a no-code, low-code node.
Having that flow overview and the no-code, low-code nodes makes my work easier by allowing me to use a simple function without coding directly, meaning I can avoid using Python. In 80% of the project, we are using Python, but for very simple steps, we also use a low-code, no-code node, which can be simpler for users that are not technical and may want to do some preprocessing steps.
Dataiku has positively impacted my organization, but it is a tool that is very similar to others and it helps for what I mentioned before and not for other areas. The ability to use low-code or no-code nodes is more a convenience in that case, mainly for a non-technical user. We deliver this kind of solution for a client where the user is not so technical, and for this reason, it is better to have this kind of flow and tool.
What needs improvement?
To improve Dataiku, it could enhance its visualization features, as it is not possible in Dataiku to create direct visualizations or to integrate a web app directly or in a simpler way as it is possible for a preprocessing step. Visualization and integration are the main areas I would like to see enhanced.
In my experience, Dataiku can be more stable.
For how long have I used the solution?
I have been using Dataiku for two years.
What do I think about the stability of the solution?
In my experience, Dataiku can be more stable.
What do I think about the scalability of the solution?
Dataiku's scalability is not one of the best solutions to scale.
Which solution did I use previously and why did I switch?
We used a lot of other solutions before Dataiku and we switched only so that non-technical users can improve and maintain this kind of flow.
What other advice do I have?
My advice to others looking into using Dataiku is to use it for a simple flow in data science and to teach how to make a data science project or flow for non-technical users. I would rate this product a 7 out of 10.
Which deployment model are you using for this solution?
Public Cloud