

Alteryx and Amazon SageMaker compete in the data analytics and machine learning category. Based on the comparisons, Alteryx seems to have an upper hand in ease of use and integration for business users with its codeless system, while SageMaker is advantageous for extensive machine learning projects with strong AWS integration.
Features: Alteryx stands out for its ease of use with a codeless, drag-and-drop interface that facilitates data blending and integration with tools like Tableau. It supports predictive analytics and can efficiently process large data volumes. Amazon SageMaker provides powerful machine learning capabilities, including model deployment, AutoML, and hyperparameter tuning, with seamless integration with AWS services.
Room for Improvement: Alteryx could improve its visualization features and in-database tool functionality, while users suggest more automated data profiling and flexible pricing. SageMaker could benefit from simplifying setup processes, enhancing cost transparency, and improving customer support documentation, especially for diverse data pipelines.
Ease of Deployment and Customer Service: Alteryx is suitable for on-premises and hybrid environments, offering strong community support, though some users experience delayed response times. Amazon SageMaker, operating mainly in the public cloud, benefits from AWS’s ecosystem but could improve its documentation and onboarding process. Both offer responsive customer support, though SageMaker's premium tiers can be costly.
Pricing and ROI: Alteryx has a higher upfront cost with licenses starting at $5,000 annually, providing quick ROI through data handling efficiencies. Its comprehensive capabilities justify the high fees for many businesses. SageMaker follows a pay-as-you-go model, which can become expensive for continuous use, but offers valuable investment for machine learning projects due to efficient AWS integration. Careful cost management is essential to maximize ROI.
Tasks that earlier took hours in Excel or SQL are now completed in minutes.
Alteryx would actually save time and a lot of money and effort for the team and increase efficiency.
Alteryx helps familiarize managers with artificial intelligence-driven possibilities.
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.
Amazon SageMaker definitely provides ROI.
I contacted customer support once or twice, and they were quick to respond.
The customer service was not good because we weren't premium support users.
Customer support is good since I've had no issues and can easily contact representatives who respond promptly.
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.
Alteryx can be scaled to different machines or scaled up with different servers and deployed in the cloud.
Alteryx is scalable for most enterprise analytics and data preparation workloads.
Alteryx is scalable, and I would give it eight out of ten.
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.
I didn't need to reach out to Alteryx for support because available documents usually provide enough information to resolve issues.
I have not encountered any lagging, crashing, or instability in the system during these three months of usage.
I have not noticed anything with the product itself, but with some of the connectors they have provided, there are some issues.
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.
The tool could include more native connectors, such as for global ERPs, instead of requiring additional fees for these connections.
The support structure changed; initially, we received great support, however, it later became less reliable due to licensing issues and a tiered support system.
The additional features that Alteryx needs to work on to make it more competitive include better collaboration and easier integration through API.
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 price is very high, with licensing typically starting around five thousand dollars plus user per year.
Alteryx is more cost-effective compared to Informatica licenses, offering savings.
It has a fair price when considering a larger-scale implementation.
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.
Alteryx not only represents data but also supports decision-making by suggesting the next steps.
Analysts who do not have any coding experience can still work on the transformation and preparation of data, which is quite useful.
Alteryx includes built-in tools such as drive time analysis and linear regression, which are much harder to achieve in standard BI tools such as Power BI or Tableau.
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.
| Product | Mindshare (%) |
|---|---|
| Amazon SageMaker | 3.5% |
| Alteryx | 3.8% |
| Other | 92.7% |

| Company Size | Count |
|---|---|
| Small Business | 32 |
| Midsize Enterprise | 16 |
| Large Enterprise | 54 |
| Company Size | Count |
|---|---|
| Small Business | 12 |
| Midsize Enterprise | 11 |
| Large Enterprise | 18 |
Alteryx provides user-friendly, no-code tools for data blending, preparation, and analysis. Its drag-and-drop interface and in-database capabilities simplify integration with data sources while maintaining data integrity.
Alteryx offers a comprehensive suite for automation of data workflows, reducing manual tasks and enhancing processing efficiency. Known for robust predictive and spatial analytics, it effectively handles large datasets. The platform's flexibility allows for custom script deployments, supported by a strong community. However, Alteryx faces challenges with high pricing, lack of cloud support, and limited data visualization tools. Users express a need for more in-built data science functionalities, improved API integration, and a smoother learning curve. Connectivity and documentation gaps, along with complex workflows, are noted concerns, suggesting areas for enhancement. Alteryx is widely used for tasks like ETL processes, data preparation, predictive modeling, and report generation, supporting functions like financial projections and spatial analysis.
What features define Alteryx?Alteryx is implemented across industries for diverse needs such as anomaly detection in finance, customer segmentation in marketing, and tax automation in auditing. Teams leverage its capabilities for data blending and predictive modeling to enhance operational efficiency and address specific business needs effectively.
Amazon SageMaker accelerates machine learning workflows by offering features like Jupyter Notebooks, AutoML, and hyperparameter tuning, while integrating seamlessly with AWS services. It supports flexible resource selection, effective API creation, and smooth model deployment and scaling.
Providing a comprehensive suite of tools, Amazon SageMaker simplifies the development and deployment of machine learning models. Its integration with AWS services like Lambda and S3 enhances efficiency, while SageMaker Studio, featuring Model Monitor and Feature Store, supports streamlined workflows. Users call for improvements in IDE maturity, pricing, documentation, and enhanced serverless architecture. By addressing scalability, big data integration, GPU usage, security, and training resources, SageMaker aims to better assist in machine learning demands and performance optimization.
What features does Amazon SageMaker offer?In industries like finance, retail, and healthcare, Amazon SageMaker supports training and deploying machine learning models for outlier detection, image analysis, and demand forecasting. It aids in chatbot implementation, recommendation systems, and predictive modeling, enhancing data science collaboration and leveraging compute resources efficiently. Tools like Jupyter notebooks, Autopilot, and BlazingText facilitate streamlined AI model management and deployment, increasing productivity and accuracy in industry-specific applications.
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