

Dataiku and Darwin compete in data management and machine learning solutions. Dataiku has the upper hand in ease of use and collaboration features, while Darwin excels in technical model performance.
Features: Dataiku offers an intuitive interface with collaborative tools, seamless integration with various data sources, and facilitates diverse business needs. Darwin focuses on automated model selection, robust model delivery, and reduced manual intervention to enhance machine learning outputs.
Room for Improvement: Dataiku could improve its advanced analytics capabilities, automation features, and scalability for large datasets. Darwin may enhance its user interface for better accessibility, expand data source integration, and offer more comprehensive support resources.
Ease of Deployment and Customer Service: Dataiku provides flexible deployment options with personalized support and training. Darwin streamlines deployment through a cloud-based approach, suitable for tech-savvy organizations seeking rapid implementation.
Pricing and ROI: Dataiku's pricing is premium, aiming for high ROI through comprehensive features and collaboration efficiency. Darwin offers a performance-based pricing model that appeals to businesses focused on immediate machine learning results, reflecting ROI through innovative modeling gains.
The market is competitive, and Dataiku must adopt a consumption-based model instead of the current monthly model.
I consider the return on investment with Dataiku valuable because for us, it is one single platform where all our data scientists come together and work on any model building, so it is collaboration, plus having everything in one place, organized, having proper project management, and then built-in capabilities which help to facilitate model building.
In terms of ROI, the use of Dataiku simplifies the architecture of customers, which helps them to decommission some of their existing tools;
Dataiku partners with local industry experts who understand the business better and provide support.
The support team does not provide adequate assistance.
They should not take the complaints so lightly.
Dataiku is quite scalable, as long as I can pay for more licenses, there is no technical limitation.
As for stability and reliability, so far so good; after the installation, I really had no problems.
In terms of stabilization, if my data has no outlier creation in the raw data, then it is quite stable.
Someone who needs to do coding can do it, and someone who does not know coding can also build solutions.
The license is very expensive.
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.
There are no extra expenses beyond the existing licensing cost.
I find the pricing of Dataiku quite affordable for our customers, as they are usually large companies.
The pricing for Dataiku is very high, which is its biggest downside.
It offers most of the capabilities required for data science, MLOps, and LLMOps.
Dataiku primarily enhances the speed at which our customers can develop or train their machine learning models because it is a drag-and-drop platform.
This feature is useful because it simplifies tasks and eliminates the need for a data scientist.
| Product | Market Share (%) |
|---|---|
| Dataiku | 8.0% |
| Darwin | 1.0% |
| Other | 91.0% |

| Company Size | Count |
|---|---|
| Small Business | 6 |
| Large Enterprise | 2 |
| Company Size | Count |
|---|---|
| Small Business | 4 |
| Midsize Enterprise | 2 |
| Large Enterprise | 11 |
SparkCognition builds leading artificial intelligence solutions to advance the most important interests of society. We help customers analyze complex data, empower decision making, and transform human and industrial productivity with award-winning machine learning technology and expert teams focused on defense, IIoT, and finance.
Dataiku Data Science Studio is acclaimed for its versatile capabilities in advanced analytics, data preparation, machine learning, and visualization. It streamlines complex data tasks with an intuitive visual interface, supports multiple languages like Python, R, SQL, and scales efficiently for large dataset handling, boosting organizational efficiency and collaboration.
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