IBM ILOG CPLEX Optimization Studio vs. NVIDIA RAPIDS

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
IBM ILOG CPLEX Optimization Studio
Score 9.3 out of 10
N/A
IBM® ILOG® CPLEX® Optimization Studio is a prescriptive analytics solution that enables rapid development and deployment of decision optimization models using mathematical and constraint programming.
$199
Per User Per Month
NVIDIA RAPIDS
Score 9.2 out of 10
N/A
NVIDIA RAPIDS is an open source software library for data science and analytics performed across GPUs. Users can run data science workflows with high-speed GPU compute and parallelize data loading, data manipulation, and machine learning for 50X faster end-to-end data science pipelines.N/A
Pricing
IBM ILOG CPLEX Optimization StudioNVIDIA RAPIDS
Editions & Modules
Developer Subscription
$199.00
Per User Per Month
No answers on this topic
Offerings
Pricing Offerings
IBM ILOG CPLEX Optimization StudioNVIDIA RAPIDS
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 ILOG CPLEX Optimization StudioNVIDIA RAPIDS
Top Pros

No answers on this topic

Top Cons
Features
IBM ILOG CPLEX Optimization StudioNVIDIA RAPIDS
Platform Connectivity
Comparison of Platform Connectivity features of Product A and Product B
IBM ILOG CPLEX Optimization Studio
8.0
2 Ratings
6% below category average
NVIDIA RAPIDS
9.1
2 Ratings
7% above category average
Connect to Multiple Data Sources9.02 Ratings9.62 Ratings
Extend Existing Data Sources7.02 Ratings8.82 Ratings
Automatic Data Format Detection8.02 Ratings9.02 Ratings
MDM Integration8.02 Ratings9.01 Ratings
Data Exploration
Comparison of Data Exploration features of Product A and Product B
IBM ILOG CPLEX Optimization Studio
10.0
2 Ratings
17% above category average
NVIDIA RAPIDS
9.4
2 Ratings
11% above category average
Visualization10.02 Ratings9.42 Ratings
Interactive Data Analysis10.02 Ratings9.42 Ratings
Data Preparation
Comparison of Data Preparation features of Product A and Product B
IBM ILOG CPLEX Optimization Studio
7.3
2 Ratings
12% below category average
NVIDIA RAPIDS
8.9
2 Ratings
8% above category average
Interactive Data Cleaning and Enrichment5.01 Ratings7.82 Ratings
Data Transformations7.01 Ratings9.42 Ratings
Data Encryption8.02 Ratings9.01 Ratings
Built-in Processors9.02 Ratings9.42 Ratings
Platform Data Modeling
Comparison of Platform Data Modeling features of Product A and Product B
IBM ILOG CPLEX Optimization Studio
8.0
2 Ratings
6% below category average
NVIDIA RAPIDS
9.2
2 Ratings
8% above category average
Multiple Model Development Languages and Tools10.02 Ratings9.01 Ratings
Automated Machine Learning5.01 Ratings9.42 Ratings
Single platform for multiple model development8.02 Ratings9.42 Ratings
Self-Service Model Delivery9.01 Ratings9.01 Ratings
Model Deployment
Comparison of Model Deployment features of Product A and Product B
IBM ILOG CPLEX Optimization Studio
10.0
2 Ratings
15% above category average
NVIDIA RAPIDS
9.2
2 Ratings
7% above category average
Flexible Model Publishing Options10.02 Ratings9.42 Ratings
Security, Governance, and Cost Controls10.02 Ratings9.01 Ratings
Best Alternatives
IBM ILOG CPLEX Optimization StudioNVIDIA RAPIDS
Small Businesses
IBM SPSS Modeler
IBM SPSS Modeler
Score 7.8 out of 10
IBM SPSS Modeler
IBM SPSS Modeler
Score 7.8 out of 10
Medium-sized Companies
Mathematica
Mathematica
Score 8.2 out of 10
Mathematica
Mathematica
Score 8.2 out of 10
Enterprises
IBM SPSS Modeler
IBM SPSS Modeler
Score 7.8 out of 10
IBM SPSS Modeler
IBM SPSS Modeler
Score 7.8 out of 10
All AlternativesView all alternativesView all alternatives
User Ratings
IBM ILOG CPLEX Optimization StudioNVIDIA RAPIDS
Likelihood to Recommend
9.0
(2 ratings)
10.0
(2 ratings)
Usability
9.0
(1 ratings)
-
(0 ratings)
Support Rating
7.0
(1 ratings)
-
(0 ratings)
User Testimonials
IBM ILOG CPLEX Optimization StudioNVIDIA RAPIDS
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.
Read full review
NVIDIA
NVIDIA RAPIDS drastically improves our productivity with near-interactive data science. And increases machine learning model accuracy by iterating on models faster and deploying them more frequently. It gives us the freedom to execute end-to-end data science and analytics pipelines.
Read full review
Pros
IBM
  • Linear Programming
  • Mixed-Integer Linear Programming
  • Non-Linear Convex-Optimization
  • Visualization
  • Shadow Price Analysis
  • Parameter Tuning
Read full review
NVIDIA
  • Visualization
  • Deep learning pipeline
  • State of the art libraries
Read full review
Cons
IBM
  • Data handling from different sources like Note Pad, etc.
  • Large size of MILP problems.
  • Various parameters to set.
Read full review
NVIDIA
  • Its not flexible and cost effective for all sizes of organizations.
  • I appreciate it has hassle-free integration.
Read full review
Usability
IBM
It's nice to use and with good optimization.
Read full review
NVIDIA
No answers on this topic
Support Rating
IBM
Honestly, to say, I never contacted CPLEX but used its forum to know/clarify any issues I faced.
Read full review
NVIDIA
No answers on this topic
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.
Read full review
NVIDIA
RAPIDS GPU accelerates machine learning to make the entire data science and analytics workflows run faster, also helps build databases and machine learning applications effectively. It also allows faster model deployment and iterations to increase machine learning model accuracy. The great value of money.
Read full review
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
Read full review
NVIDIA
  • Efficient way to complete tasks
  • De-facto GPUs standard
Read full review
ScreenShots