IBM watsonx.ai vs. SAP Predictive Analytics

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
IBM watsonx.ai
Score 8.7 out of 10
N/A
Watsonx.ai is part of the IBM watsonx platform that brings together new generative AI capabilities, powered by foundation models, and traditional machine learning into a studio spanning the AI lifecycle. Watsonx.ai can be used to train, validate, tune, and deploy generative AI, foundation models, and machine learning capabilities, and build AI applications with less time and data.
$0
SAP Predictive Analytics
Score 7.0 out of 10
N/A
SAP Predictive Analytics is, as the name would suggest, a statistical analysis and data mining platform that can be deployed with SAP HANA.N/A
Pricing
IBM watsonx.aiSAP Predictive Analytics
Editions & Modules
Free Trial
$0
ML functionality (20 CUH limit /month); Inferencing (50,000 tokens / month)
Standard
$1,050
Monthly tier fee; additional usage based fees
Essentials
Contact Sales
Usage based fees
No answers on this topic
Offerings
Pricing Offerings
IBM watsonx.aiSAP Predictive Analytics
Free Trial
YesNo
Free/Freemium Version
YesNo
Premium Consulting/Integration Services
YesNo
Entry-level Setup FeeNo setup feeNo setup fee
Additional DetailsPricing for watsonx.ai includes: model inference per 1000 tokens and ML tools and ML runtimes based on capacity unit hours.
More Pricing Information
Community Pulse
IBM watsonx.aiSAP Predictive Analytics
Features
IBM watsonx.aiSAP Predictive Analytics
AI Development
Comparison of AI Development features of Product A and Product B
IBM watsonx.ai
5.5
1 Ratings
24% below category average
SAP Predictive Analytics
-
Ratings
Machine learning frameworks5.51 Ratings00 Ratings
Data management4.51 Ratings00 Ratings
Data monitoring and version control4.51 Ratings00 Ratings
Automated model training4.51 Ratings00 Ratings
Managed scaling6.41 Ratings00 Ratings
Model deployment6.41 Ratings00 Ratings
Security and compliance6.41 Ratings00 Ratings
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User Ratings
IBM watsonx.aiSAP Predictive Analytics
Likelihood to Recommend
9.1
(32 ratings)
9.0
(3 ratings)
Likelihood to Renew
6.4
(1 ratings)
-
(0 ratings)
Usability
7.8
(6 ratings)
6.0
(1 ratings)
Support Rating
-
(0 ratings)
7.0
(1 ratings)
Ease of integration
6.4
(2 ratings)
-
(0 ratings)
Product Scalability
9.1
(1 ratings)
-
(0 ratings)
User Testimonials
IBM watsonx.aiSAP Predictive Analytics
Likelihood to Recommend
IBM
I have built a code accelerator tool for one of the IBM product implementation. Although there was a heavy lifting at the start to train the model on specifics of the packaged solution library and ways of working; the efficacy of the model is astounding. Having said that, watsonx.ai is very well suited for customer service automation, healthcare data analytics, financial fraud detection, and sentiment analysis kind of projects. The Watsonx.ai look and feel is little confusing but I understand over a period of time , it will improve dramatically as well. I do feel that Watsonx.ai has certain limitations from cross-platform deployment flexibility. If an organization is deeply invested in a multi-cloud environment, Watson's integration on other cloud platforms may not be seamless comported to other AI platforms.
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SAP
It's a great tool to merge actual data analysis (which Lumira doesn't do that well) with visualization (which Lumira does well) - so it can be seen as Lumira for data analysts. However, a lot of the 'predictive' side is hidden/black box which can be frustrating for those analysts, so you could argue it is too complex for casual users, but too 'black box' for analysts.
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Pros
IBM
  • It allows specialists to apply several base models for specific subtasks in the field of NLP.
  • Gives the availability of many models developed for AI enhancement for different solutions.
  • Has incorporated functionality for data governance and security to support access to AI tools by multiple users.
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SAP
  • It doesn't require you to have a Ph.D. to build models!
  • You can use it to address a very large and wide dataset without worrying about sampling.
  • Automation is in the product DNA. You can prepare your data, ingest it into the "Kernel", then get insights about what was found, decide to publish it and schedule scoring tasks or model refresh in the same product.
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Cons
IBM
  • IBM watsonx.ai is expensive than other platforms.
  • Limited integraions though it has many but still some tools integrations not there for medical usecase
  • Its little difficult to learn as right now not many open reseouces
  • Community is not that strong to get any answer
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SAP
  • Working with this software is very simple and enjoyable for me as [an] IT consultant and expert, but it is a bit complicated for novice users.
  • Some big data takes more time‌to load, which I think could be faster
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Likelihood to Renew
IBM
I still don't have enough experience, but i have seen a lot of demos and i have made some real world scenarios and so far so long every thing looks fine. I was at IBM Think 2025 and IBM TechXchange 2025 and the labs were really usefull and simple to understand.
Read full review
SAP
No answers on this topic
Usability
IBM
I needed some time to understand the different parts of the web UI. It was slightly overwhelming in the beginning. However, after some time, it made sense, and I like the UI now. In terms of functionality, there are many useful features that make your life easy, like jumping to a section and giving me a deployment space to deploy my models easily.
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SAP
the UI is a bit dated and available as a desktop tool mostly.
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Support Rating
IBM
I still don't have enough experience, but i have seen a lot of demos and i have made some real world scenarios and so far so long every thing looks fine. I was at IBM Think 2025 and IBM TechXchange 2025 and the labs were really usefull and simple to understand.
Read full review
SAP
The documentation provides an explanation about what features are available but not necessarily what's happening behind the scenes. On the other side, the "community" has grown since the acquisition and most questions are properly addressed by SAP folks. Since the "product maintenance" mode announcement was made, there wasn't much new content published except on the Smart Predict side (which is built by the SAP Predictive Analytics team)
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Alternatives Considered
IBM
IBM watsonx.ai has been far superior to that of Chat GPT AI. the UI elements prompt responses and overall execution of the AI was much better and more accurate compared to the competition. I can not recommend using this platform enough. Great job IBM. I hope the team behind this project continues to grow and prosper.
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SAP
We have typically used Spotfire for data analysis but decided to move to SAP Business Objects due to its innate connection with SAP. I found Lumira to be good for visualizations but it is not meant for data analysis. Therefore, we have introduced Predictive Analytics to see if it can fill that gap. So far, it's been far less intuitive than Spotfire to get started, and as far as I am aware so far, it does not bring many additional capabilities. I do, however, like that it utilizes the Lumira look/feel and integrates very well.
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Scalability
IBM
I still don't have enough experience, but i have seen a lot of demos and i have made some real world scenarios and so far so long every thing looks fine. I was at IBM Think 2025 and IBM TechXchange 2025 and the labs were really usefull and simple to understand.
Read full review
SAP
No answers on this topic
Return on Investment
IBM
  • Time saving to set up the infrastructure - without watsonx.ai we would have had to set up everything individually
  • The first point translates directly into cost savings
  • The compliance aspect was a game changer for us and provided us with the confidence to focus all our efforts only on IBM watsonx.ai
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SAP
  • Proper forecasting increases our credibility with partners and customers
  • Forecasting determines the amount of investment in each sector and reduces the cost of additional costs
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ScreenShots

IBM watsonx.ai Screenshots

Screenshot of the foundation models available in watsonx.ai. Clients have access to IBM selected open source models from Hugging Face, as well as other third-party models, and a family of IBM-developed foundation models of different sizes and architectures.Screenshot of the Prompt Lab in watsonx.ai, where AI builders can work with foundation models and build prompts using prompt engineering techniques in watsonx.ai to support a range of Natural Language Processing (NLP) type tasks.Screenshot of the Tuning Studio in watsonx.ai, where AI builders can tune foundation models with labeled data for better performance and accuracy.Screenshot of the data science toolkit in watsonx.ai where AI builders can build machine learning models automatically with model training, development, visual modeling, and synthetic data generation.