Overview
Dataiku 3-Minute Demo
Dataiku 3-Minute Demo

Product video
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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Dimension | Description | Cost/12 months |
|---|---|---|
Dataiku | Contact us for pricing | $1.00 |
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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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Customer reviews
Dataiku Speeds Up Repeatable Marketing Data Workflows
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.
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.
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.
Powerful Collaboration, but Can Feel Bulky and Resource-Heavy
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.
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.