Dataiku and Amazon SageMaker are two prominent platforms competing in the domain of data science and machine learning. For businesses prioritizing ease of use and collaboration, Dataiku appears to have the advantage. However, for experienced data teams prioritizing scalability and performance, Amazon SageMaker stands out with robust integrations and comprehensive features.
Features: Dataiku is praised for its visual interface that aids in workflow creation and its ability to integrate seamlessly with various data sources. It also supports a range of languages, enhancing its versatility as a user-friendly tool. On the other hand, SageMaker offers advanced machine learning capabilities, coupled with an integrated development environment ideal for code-first approaches. Its automation tools for model tuning contribute to SageMaker's position as a robust solution for complex tasks and deployments.
Room for Improvement: Dataiku could enhance its scalability options for larger enterprises, streamline integration with more cloud services, and provide more advanced automation features. SageMaker could improve by simplifying its deployment processes, offering more intuitive support options, and reducing the complexity of its interface for users less familiar with AWS services.
Ease of Deployment and Customer Service: Dataiku's intuitive deployment process requires little technical expertise, which, combined with responsive customer support, makes it accessible for most businesses. Conversely, while SageMaker offers a more complex deployment model, it allows for extensive customization and scalability, making it ideal for larger operations. The platform benefits from AWS's comprehensive service structure, but it requires experienced users to navigate its full potential.
Pricing and ROI: Dataiku adopts a straightforward pricing model, which tends to result in predictable costs and strong ROI through collaborative efficiency. For SageMaker, the usage-based pricing can cater well to scaling needs but poses a risk of unforeseen expenses. Despite its complexity and learning curve, the advanced features justify the costs for organizations able to maximize its potential, particularly in large-scale environments.
The return on investment varies by use case and offers significant value in revenue increases and cost saving capabilities, especially in real time fraud detection and targeted advertisements.
The market is competitive, and Dataiku must adopt a consumption-based model instead of the current monthly model.
The technical support from AWS is excellent.
The support is very good with well-trained engineers.
The response time is generally swift, usually within seven to eight hours.
Dataiku partners with local industry experts who understand the business better and provide support.
The support team does not provide adequate assistance.
The customer service team is helpful and responsive, more or less on time.
The availability of GPU instances can be a challenge, requiring proper planning.
It works very well with large data sets from one terabyte to fifty terabytes.
Amazon SageMaker is scalable and works well from an infrastructure perspective.
There are issues, but they are easily detectable and fixable, with smooth error handling.
The product has been stable and scalable.
I rate the stability of Amazon SageMaker between seven and eight.
In terms of stabilization, if my data has no outlier creation in the raw data, then it is quite stable.
Having all documentation easily accessible on the front page of SageMaker would be a great improvement.
This would empower citizen data scientists to utilize the tool more effectively since many data scientists do not have a core development background.
Integration of the latest machine learning models like the new Amazon LLM models could enhance its capabilities.
The license is very expensive.
I would love for Dataiku to allow more flexibility with code-based components and provide the possibility to extend it by developing and integrating custom components easily with existing ones.
Dataiku's pricing is very high, and commercial transparency is a challenge.
The cost for small to medium instances is not very high.
For a single user, prices might be high yet could be cheaper for user-managed services compared to AWS-managed services.
The pricing can be up to eight or nine out of ten, making it more expensive than some cloud alternatives yet more economical than on-premises setups.
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.
SageMaker supports building, training, and deploying AI models from scratch, which is crucial for my ML project.
They offer insights into everyone making calls in my organization.
The most valuable features include the ML operations that allow for designing, deploying, testing, and evaluating models.
This feature is useful because it simplifies tasks and eliminates the need for a data scientist.
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.
It offers most of the capabilities required for data science, MLOps, and LLMOps.
| Product | Market Share (%) |
|---|---|
| Amazon SageMaker | 5.7% |
| Dataiku | 11.7% |
| Other | 82.6% |
| Company Size | Count |
|---|---|
| Small Business | 12 |
| Midsize Enterprise | 11 |
| Large Enterprise | 16 |
| Company Size | Count |
|---|---|
| Small Business | 4 |
| Midsize Enterprise | 1 |
| Large Enterprise | 8 |
Amazon SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. Amazon SageMaker removes all the barriers that typically slow down developers who want to use machine learning.
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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