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    Dataiku for Enterprise AI (Non U.S. Markets)

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    Sold by: Dataiku 
    Deployed on AWS
    Accelerate Enterprise AI with Dataiku on AWS
    4.4

    Overview

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    Dataiku is the Platform for AI Success, the enterprise orchestration layer for building, deploying, and governing AI. In a single environment, teams design and operate analytics, machine learning, and AI agents with the transparency, collaboration, and control enterprises require.

    • 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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    Pricing

    Dataiku for Enterprise AI (Non U.S. Markets)

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    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.

    12-month contract (1)

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    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.

    Custom pricing options

    Request a private offer to receive a custom quote.

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    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) .

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    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.

    Usage information

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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.

    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.

    Product comparison

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    Accolades

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    Top
    10
    In ML Solutions
    Top
    10
    In Databases & Analytics Platforms, ML Solutions, Data Analytics

    Customer reviews

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    Sentiment is AI generated from actual customer reviews on AWS and G2
    Reviews
    Functionality
    Ease of use
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    Cost effectiveness
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    Overview

     Info
    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.

    Contract

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    Standard contract
    No
    No
    No

    Customer reviews

    Ratings and reviews

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    4.4
    212 ratings
    5 star
    4 star
    3 star
    2 star
    1 star
    63%
    31%
    6%
    0%
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    6 AWS reviews
    |
    206 external reviews
    External reviews are from G2  and PeerSpot .
    Michele C.

    Dataiku Speeds Up Repeatable Marketing Data Workflows

    Reviewed on May 22, 2026
    Review provided by G2
    What do you like best about the product?
    What I like most about Dataiku is how much faster it lets me move on marketing data projects for clients. In my day-to-day work as a digital marketing consultant, I often have to bring together data from multiple sources—CRM exports, campaign performance reports, website analytics, sales data, and sometimes offline datasets. Dataiku gives me a structured environment to clean, connect, and analyze everything in one place, without having to rebuild the entire process from scratch each time.

    I use the visual workflows regularly because they make the end-to-end process far more transparent. Rather than working only in spreadsheets or in isolated scripts, I can see every step of the data preparation flow and explain it clearly to clients or internal teams. This is especially helpful when I need to show exactly how a lead scoring model, a customer segmentation analysis, or a campaign performance dataset was created.

    Another thing I really value is the balance between no-code and code options. For everyday consulting work, it’s practical: I can move quickly with visual recipes for common tasks, and then go deeper with SQL or Python when the analysis needs more flexibility. That saves time and makes it easier to adapt the workflow to the complexity of each project.

    Dataiku also improves collaboration with non-technical stakeholders. When I’m working with marketing managers or sales teams, they don’t always need the technical details, but they do need to trust the output. Having a clear, documented workflow makes conversations smoother and helps translate analysis into concrete marketing decisions.

    Overall, the biggest benefit for me is that Dataiku turns complex data preparation and analysis into a repeatable consulting workflow. It helps me spend less time on manual data cleaning and more time interpreting results, spotting opportunities, and recommending actions to improve campaign performance, customer targeting, and ROI.
    What do you dislike about the product?
    What I dislike about Dataiku is that it can feel a bit heavy at the beginning, especially if the team is not already familiar with data workflows, data preparation logic or machine learning concepts. As a consultant, I can usually navigate the platform quite well, but when I involve clients or marketing teams who are less technical, there is sometimes a learning curve before they feel comfortable using it independently.

    The pricing can also be a limitation, especially for smaller clients or companies that are still at an early stage in their data maturity. Dataiku can deliver strong value when it is used regularly across multiple projects, teams and data sources, but for a smaller marketing team that only needs occasional analysis, it may feel like a significant investment. The ROI is much clearer when the company is ready to operationalize data workflows, not just run one-off reports.

    In terms of onboarding, I think the platform requires a structured introduction to get the most out of it. There are many features, which is a strength, but it can also be overwhelming at first. For some clients, I need to spend extra time explaining not only how the tool works, but also how to think in terms of reusable data pipelines instead of simple spreadsheet-based analysis.

    Regarding AI and machine learning, the capabilities are powerful, but they still require good data quality and a clear business objective. Dataiku can help a lot with automation and predictive models, but it does not replace the strategic work of defining the right question, selecting the right variables and interpreting the results correctly. In my daily work, I still need to guide clients carefully so they do not treat AI outputs as automatic answers without proper validation.

    So overall, my main dislike is not about a single missing feature, but about the complexity that comes with such a complete platform. It is very useful, but it needs the right level of adoption, training and business commitment to fully justify the investment.
    What problems is the product solving and how is that benefiting you?
    Dataiku helps me solve one of the main problems I face as a digital marketing consultant: fragmented data. In many client projects, marketing data is spread across different platforms, spreadsheets, CRM systems, advertising accounts and analytics tools. Before using a structured platform like Dataiku, a lot of time could be lost just cleaning files, matching columns, checking inconsistencies and preparing the data before any real analysis could begin.

    With Dataiku, I can create more repeatable workflows for tasks like campaign performance analysis, customer segmentation, lead scoring, churn analysis and ROI reporting. This benefits me because I do not have to start from zero every time a client sends updated data. Once the workflow is built, I can refresh the inputs, review the outputs and focus more on insights and recommendations.

    It also helps me reduce manual errors. When working only with spreadsheets, it is easy to lose track of formulas, versions or manual changes. Dataiku makes the process more structured and transparent, so I can better control how the data is transformed and explain the logic behind the final results to clients.

    Another important benefit is that it helps me move from simple reporting to more advanced decision support. Instead of only showing what happened in a campaign, I can help clients understand patterns, identify high-value segments, predict possible outcomes and prioritize marketing actions more effectively.

    Overall, Dataiku saves time, improves the reliability of my analysis and helps me deliver more strategic value. It allows me to spend less energy on repetitive data preparation and more time advising clients on what to do next.
    Information Technology and Services

    Powerful Collaboration, but Can Feel Bulky and Resource-Heavy

    Reviewed on May 19, 2026
    Review provided by G2
    What do you like best about the product?
    What I like most about Dataiku is the way it balances ease of use with strong analytics and AI capabilities. It allows business users, analysts, and data scientists to collaborate smoothly within the same platform. The combination of visual workflows and the flexibility to code when needed makes it straightforward to go from experimentation to production more quickly.
    What do you dislike about the product?
    One thing I dislike about Dataiku is that it can feel bulky and resource-heavy at times.
    It tends to save/cache data in many places, which can make workflows harder to manage and slower to navigate.
    For hardcore technical teams that prefer lightweight, code-first tooling, it may feel slower and more restrictive than working directly with native engineering stacks.
    What problems is the product solving and how is that benefiting you?
    Dataiku solves the problem of fragmented data, disconnected teams, and slow AI deployment by bringing data prep, analytics, and machine learning into one platform.
    It benefits me by making collaboration between business and technical teams much smoother and reducing the time needed to move projects from experimentation to production.
    Pharmaceuticals

    Template Solutions That Speed Up Implementations

    Reviewed on Apr 17, 2026
    Review provided by G2
    What do you like best about the product?
    Template solutions to speed up implementations
    What do you dislike about the product?
    The template solutions don't expose the code behind so it's sometimes hard to understand how the features work (documentation is not sufficient)
    What problems is the product solving and how is that benefiting you?
    Quick prototyping of solutions
    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
    View all reviews