Sold by: Dataiku
Deployed on AWS
Accelerate Enterprise AI with Dataiku on AWS
4.4
Overview
Video
Dataiku 3-Minute Demo
Video
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.
Details
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Introducing multi-product solutions
You can now purchase comprehensive solutions tailored to use cases and industries.
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 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 |
|---|---|---|
Please contact us for Pricing | Please contact aws@dataiku.com to get a custom quote through Private Offer | $0.01 |
Dimensions summary
This listing uses a custom contract model rather than fixed public pricing. You do not select from set tiers or instance sizes here. Instead, you contact aws@dataiku.com to receive a quote through a Private Offer. Your price is negotiated based on your needs and delivered as a single agreed contract. This structure fits enterprise deployments where scope, users, and infrastructure vary. Pricing terms are set privately between you and the vendor, then transacted through AWS Marketplace.
Top-of-mind questions for buyers
Your quote is built around your deployment scope. Dataiku scales by users, connected data sources, and infrastructure choices. Because these vary widely, pricing is set through a Private Offer after you email aws@dataiku.com. You agree a single custom contract that reflects your team size and workload footprint.
Yes. Dataiku runs on premises, in the cloud, or hybrid, using managed or self-managed deployments. Because deployment model affects infrastructure and scope, it factors into your custom quote. Discuss your target environment with the vendor at aws@dataiku.com so the Private Offer matches how you plan to run it.
The platform brings analytics, models, agents, orchestration, and governance into one system. Govern and LLM Mesh capabilities are part of the enterprise platform rather than the evaluation setup. Since scope drives price, confirm which capabilities your contract includes when requesting a quote at aws@dataiku.com.
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Vendor refund policy
All fees are non-cancellable and non-refundable except as required by law.
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
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
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
Pavan B.
Easy No-Code Visual Flows That Connect Seamlessly to Native Servers
Reviewed on Sep 01, 2026
Review provided by G2
What do you like best about the product?
No-code or low-code visual flows that are easy to connect to native servers.
What do you dislike about the product?
The license cost is expensive, and it also requires heavy infrastructure to handle heavy flows.
What problems is the product solving and how is that benefiting you?
Operational tracking and clinical trial analytics.
Rythm G.
All-in-One Data Science Platform That Streamlines Workflows and Collaboration
Reviewed on Aug 31, 2026
Review provided by G2
What do you like best about the product?
What I like most about Dataiku is that it combines data preparation, analysis, visualization, and machine learning in a single platform. The visual workflow makes it easy to build and follow data pipelines without needing to write code for every step, while still offering flexibility for people who prefer working in Python or SQL. I also find its integrations with different data sources helpful, and the interface makes it straightforward to collaborate with others and share workflows. Overall, it streamlines the data science process and reduces the need to switch between multiple tools.
What do you dislike about the product?
One area that could be improved is the learning curve for some of the more advanced features. Although the visual interface is helpful, it can still take new users a while to understand how the different components and workflows fit together. Some of the advanced integrations and AI features would also benefit from clearer documentation, along with more beginner-friendly examples that show how to use them in practice. Performance can occasionally vary depending on the complexity and size of a workflow, so more guidance on optimizing larger projects would be useful as well.
What problems is the product solving and how is that benefiting you?
Dataiku simplifies the end-to-end data science workflow by bringing data preparation, analysis, visualization, and machine learning into a single environment. Rather than switching between multiple tools at different stages of a project, I can keep workflows and experiments organized in one place. The visual interface is especially helpful for quickly exploring data and building workflows, and the option to use Python or SQL adds flexibility when I need more advanced analysis. Overall, it’s easier to experiment, reproduce workflows, and collaborate on data-driven projects.
dnyaneshwar g.
Amazing Data Ingestion, Analysis, and Interactive Dashboards
Reviewed on Aug 29, 2026
Review provided by G2
What do you like best about the product?
The data ingestion, analysis, and interactive dashboard for visualising the data and presenting it to stakeholders are amazing. The value for money is great, so many data analysis capabilities made Dataiku a perfect solution fit for our problem.
What do you dislike about the product?
I tried integrating the Dataiku APIs into my Python project. It works flawlessly, but it still needs some improvements, and it isn’t that flexible.
What problems is the product solving and how is that benefiting you?
For my organization, Dataiku has helped us analyze well log data, and our decision-making has become much quicker. Reservoir pressure data analysis also helps onsite engineers make faster decisions.
INDRAYUDH B.
Straightforward Visual ML Workflows with Flexible Python and SQL Options
Reviewed on Aug 28, 2026
Review provided by G2
What do you like best about the product?
What I like most about Dataiku is how straightforward it makes working with data and putting together machine learning workflows. After using it for a week, I found the visual interface particularly helpful because it let me explore, clean, and transform data without needing to write code at every step. At the same time, the option to switch to Python and SQL when needed adds a lot of flexibility. Overall, it feels like a solid platform that brings data preparation, analysis, and machine learning together in one place.
What do you dislike about the product?
What I like least about Dataiku is that it can feel a bit overwhelming at first. It offers a lot of features and options, so it takes time to get comfortable with the interface and to figure out the workflow that makes the most sense. For beginners, some tasks can also seem more complicated than they need to be. After using it for a week, I still think it has a lot of potential, but I wish the initial learning curve were smoother.
What problems is the product solving and how is that benefiting you?
Dataiku helps simplify the process of preparing, analyzing, and working with data by bringing everything into one platform. Instead of switching between different tools, I can manage data, create workflows, and experiment with machine learning in one place. This saves time and makes the overall data workflow more organized and easier to manage.
samira Y.
Everything in One Platform
Reviewed on Aug 25, 2026
Review provided by G2
What do you like best about the product?
I like the fact that everything is in one platform.
What do you dislike about the product?
Advanced development requires a lot of learning, and there’s quite a bit to pick up before you feel comfortable with it.
What problems is the product solving and how is that benefiting you?
It solved the problem of fragmented data science workflows by bringing data preparation, analytics, machine learning, deployment, and governance together in one place.