IBM CPLEX Optimization Studio vs. IBM Watson Studio on Cloud Pak for Data

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
IBM CPLEX Optimization Studio
Score 9.3 out of 10
N/A
IBM CPLEX Optimization Studio is a mathematical decision optimization application, for building applications or deploying optimization models.
$199
Per User Per Month
IBM Watson Studio
Score 8.0 out of 10
N/A
IBM Watson Studio enables users to build, run and manage AI models, and optimize decisions at scale across any cloud. IBM Watson Studio enables users can operationalize AI anywhere as part of IBM Cloud Pak® for Data, the IBM data and AI platform. The vendor states the solution simplifies AI lifecycle management and accelerates time to value with an open, flexible multicloud architecture.N/A
Pricing
IBM CPLEX Optimization StudioIBM Watson Studio on Cloud Pak for Data
Editions & Modules
Developer Subscription
$199.00
Per User Per Month
No answers on this topic
Offerings
Pricing Offerings
IBM CPLEX Optimization StudioIBM Watson Studio
Free Trial
NoNo
Free/Freemium Version
NoNo
Premium Consulting/Integration Services
NoNo
Entry-level Setup FeeNo setup feeNo setup fee
Additional Details
More Pricing Information
Community Pulse
IBM CPLEX Optimization StudioIBM Watson Studio on Cloud Pak for Data
Top Pros

No answers on this topic

Top Cons
Features
IBM CPLEX Optimization StudioIBM Watson Studio on Cloud Pak for Data
Platform Connectivity
Comparison of Platform Connectivity features of Product A and Product B
IBM CPLEX Optimization Studio
8.0
2 Ratings
6% below category average
IBM Watson Studio on Cloud Pak for Data
8.1
22 Ratings
4% below category average
Connect to Multiple Data Sources9.02 Ratings8.022 Ratings
Extend Existing Data Sources7.02 Ratings8.022 Ratings
Automatic Data Format Detection8.02 Ratings9.921 Ratings
MDM Integration8.02 Ratings6.414 Ratings
Data Exploration
Comparison of Data Exploration features of Product A and Product B
IBM CPLEX Optimization Studio
10.0
2 Ratings
17% above category average
IBM Watson Studio on Cloud Pak for Data
10.0
22 Ratings
17% above category average
Visualization10.02 Ratings10.022 Ratings
Interactive Data Analysis10.02 Ratings10.022 Ratings
Data Preparation
Comparison of Data Preparation features of Product A and Product B
IBM CPLEX Optimization Studio
7.2
2 Ratings
14% below category average
IBM Watson Studio on Cloud Pak for Data
9.5
22 Ratings
14% above category average
Interactive Data Cleaning and Enrichment5.01 Ratings9.922 Ratings
Data Transformations7.01 Ratings10.021 Ratings
Data Encryption8.02 Ratings8.020 Ratings
Built-in Processors9.02 Ratings10.021 Ratings
Platform Data Modeling
Comparison of Platform Data Modeling features of Product A and Product B
IBM CPLEX Optimization Studio
8.0
2 Ratings
6% below category average
IBM Watson Studio on Cloud Pak for Data
9.5
22 Ratings
11% above category average
Multiple Model Development Languages and Tools10.02 Ratings10.021 Ratings
Automated Machine Learning5.01 Ratings9.922 Ratings
Single platform for multiple model development8.02 Ratings9.922 Ratings
Self-Service Model Delivery9.01 Ratings8.020 Ratings
Model Deployment
Comparison of Model Deployment features of Product A and Product B
IBM CPLEX Optimization Studio
10.0
2 Ratings
15% above category average
IBM Watson Studio on Cloud Pak for Data
8.0
22 Ratings
7% below category average
Flexible Model Publishing Options10.02 Ratings9.022 Ratings
Security, Governance, and Cost Controls10.02 Ratings7.022 Ratings
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User Ratings
IBM CPLEX Optimization StudioIBM Watson Studio on Cloud Pak for Data
Likelihood to Recommend
9.0
(2 ratings)
8.0
(65 ratings)
Likelihood to Renew
-
(0 ratings)
8.2
(1 ratings)
Usability
9.0
(1 ratings)
9.6
(2 ratings)
Availability
-
(0 ratings)
8.2
(1 ratings)
Performance
-
(0 ratings)
8.2
(1 ratings)
Support Rating
7.0
(1 ratings)
8.2
(1 ratings)
In-Person Training
-
(0 ratings)
8.2
(1 ratings)
Online Training
-
(0 ratings)
8.2
(1 ratings)
Implementation Rating
-
(0 ratings)
7.3
(1 ratings)
Product Scalability
-
(0 ratings)
8.2
(1 ratings)
Vendor post-sale
-
(0 ratings)
7.3
(1 ratings)
Vendor pre-sale
-
(0 ratings)
8.2
(1 ratings)
User Testimonials
IBM CPLEX Optimization StudioIBM Watson Studio on Cloud Pak for Data
Likelihood to Recommend
IBM
It is well suited for solving large-sized, mixed-integer, and integer programming problems. Now, the new version supports for Multi-Objective optimization along with some new algorithms such as Benders Decomposition. It is less appropriate for quadratic programming problems where the objective function is the product of multiple variables. However, it's very easy to code any problem.
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IBM
It has a lot of features that are good for teams working on large-scale projects and continuously developing and reiterating their data project models. Really helpful when dealing with large data. It is a kind of one-stop solution for all data science tasks like visualization, cleaning, analyzing data, and developing models but small teams might find a lot of features unuseful.
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Pros
IBM
  • Linear Programming
  • Mixed-Integer Linear Programming
  • Non-Linear Convex-Optimization
  • Visualization
  • Shadow Price Analysis
  • Parameter Tuning
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IBM
  • Integration of IBM Watson APIs such as speech to text, image recognition, personality insights, etc.
  • SPSS modeler and neural network model provide no-code environments for data scientists to build pipelines quickly.
  • Enforced best-practices set up POCs for deployment in production with a minimum of re-work.
  • Estimator validation lets data scientists test and prove different models.
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Cons
IBM
  • Data handling from different sources like Note Pad, etc.
  • Large size of MILP problems.
  • Various parameters to set.
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IBM
  • The cost is steep and so only companies with resources can afford it
  • It will be nice to have Chinese versions so that Chinese engineers can also use it easily
  • It takes a while to learn how to input different kinds of skin defects for detection
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Likelihood to Renew
IBM
No answers on this topic
IBM
because we find out that DSX results have improved our approach to the whole subject (data, models, procedures)
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Usability
IBM
It's nice to use and with good optimization.
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IBM
The UI flawlessly merges this offering by providing a neat, minimal, responsive interface
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Reliability and Availability
IBM
No answers on this topic
IBM
From time to time there are services unavailable, but we have been always informed before and they got back to work sooner than expected
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Performance
IBM
No answers on this topic
IBM
Never had slow response even on our very busy network
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Support Rating
IBM
Honestly, to say, I never contacted CPLEX but used its forum to know/clarify any issues I faced.
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IBM
I received answers mostly at once and got answered even further my question: they gave me interesting points of view and suggestion for deepening in the learning path
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In-Person Training
IBM
No answers on this topic
IBM
The trainers on the job are very smart with solutions and very able in teaching
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Online Training
IBM
No answers on this topic
IBM
The Platform is very handy and suggests further steps according my previous interests
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Implementation Rating
IBM
No answers on this topic
IBM
It surprised us with unpredictable case of use and brand new points of view
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Alternatives Considered
IBM
IBM CPLEX Optimization Studio covers wide range of problems in comparison to Gurobi and also offers a number of visualization tools for results analysis. It has better customization and parameter tuning options in comparison to Gurobi. It offers various API integrations such as Python, Java and C++ which is not the case with Gurobi.
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IBM
The main reason I personally changed over from Azure ML Studio is because it lacked any support for significant custom modelling with packages and services such as TensorFlow, scikit-learn, Microsoft Cognitive Toolkit and Spark ML. IBM Watson Studio provides these services and does so in a well integrated and easy to use fashion making it a preferable service over the other services that I have personally used.
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Scalability
IBM
No answers on this topic
IBM
It helped us in getting from 0 to DSX without getting lost
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Return on Investment
IBM
  • Faster computation leading to better internal customer relations
  • Able to solve high variable problems with ease
  • Anomaly detection became easier within business
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IBM
  • Could instantly show data driven insights to drive 20% incremental revenue over existing results
  • Still don't have a real use case for unstructured data like twitter feed
  • Some of the insights around user actions have driven new projects to automate mundane tasks
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ScreenShots