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

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.

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

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

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

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.

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.

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.

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.

I have been using Dataiku for three years.

Dataiku is stable.

Dataiku's scalability can be better.

I have never tried Dataiku's customer support.

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.

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.

I am not the person involved in the process regarding pricing, setup cost, and licensing.

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.

Public Cloud

Amazon Web Services (AWS)

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.

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.

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.

I have been using Dataiku for two years.

In my experience, Dataiku can be more stable.

Dataiku's scalability is not one of the best solutions to scale.

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.

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.

Public Cloud

Amazon Web Services  (AWS )