Caffe Deep Learning Framework vs. IBM watsonx.ai

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Caffe Deep Learning Framework
Score 7.0 out of 10
N/A
Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by Berkeley AI Research and by community contributors.N/A
IBM watsonx.ai
Score 7.7 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
Pricing
Caffe Deep Learning FrameworkIBM watsonx.ai
Editions & Modules
No answers on this topic
Essentials
$0
per month
Free Trial
$0
per month
Standard
$1,500
per month
Offerings
Pricing Offerings
Caffe Deep Learning FrameworkIBM watsonx.ai
Free Trial
NoYes
Free/Freemium Version
NoYes
Premium Consulting/Integration Services
NoYes
Entry-level Setup FeeNo setup feeNo setup fee
Additional DetailsGet started building differentiated AI assets with watsonx.ai, our studio for generative AI, foundation models and machine learning. Scale up your AI use cases as needed with integrations to watsonx.data, a fit-for-purpose data store built on an open data lakehouse architecture, and watsonx.governance (coming soon), a toolkit to accelerate responsible, transparent and explainable AI workflows. Pricing 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
Caffe Deep Learning FrameworkIBM watsonx.ai
Top Pros

No answers on this topic

Top Cons

No answers on this topic

Best Alternatives
Caffe Deep Learning FrameworkIBM watsonx.ai
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
Posit
Posit
Score 9.1 out of 10
Posit
Posit
Score 9.1 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
Caffe Deep Learning FrameworkIBM watsonx.ai
Likelihood to Recommend
4.0
(1 ratings)
7.7
(4 ratings)
User Testimonials
Caffe Deep Learning FrameworkIBM watsonx.ai
Likelihood to Recommend
Open Source
Caffe is only appropriate for some new beginners who don't want to write any lines of code, just want to use existing models for image recognition, or have some taste of the so-called Deep Learning.
Read full review
IBM
Based on my experience, I can recommend that you have a good AI management system in your company account, and if you have the money at your disposal to invest in IBM watsonx, do not hesitate. We are using API models to obviously build a work environment with sustainable flow as well. We have AI and ML lifecycle support.
Read full review
Pros
Open Source
  • Caffe is good for traditional image-based CNN as this was its original purpose.
Read full review
IBM
  • it has many Reliable tools for algorithm modeling visualization.
  • Highly secured, Integrated and all data optimized in one management
  • Easily prepared and extract data from document.
Read full review
Cons
Open Source
  • Caffe's model definition - static configuration files are really painful. Maintaining big configuration files with so many parameters and details of many layers can be a really challenging task.
  • Besides imagine and vision (CNN), Caffe also gradually adds some other NN architecture support. It doesn't play well in a recurrent domain, so we have to say variety is a problem.
  • Caffe's deployment for production is not easy. The community support and project development all mean it is almost fading out of the market.
  • The learning curve is quite steep. Although TensorFlow's is not easy to master either, the reward for Caffe is much less than the TensorFlow can offer.
Read full review
IBM
  • APIs integration could be improved.
  • steep learnings for tuning AI models
  • performance lag
Read full review
Alternatives Considered
Open Source
TensorFlow is kind of low-level API most suited for those developers who like to control the details, while Keras provides some kind of high-level API for those users who want to boost their project or experiment by reusing most of the existing architecture or models and the accumulated best practice. However, Caffe isn't like either of them so the position for the user is kind of embarrassing.
Read full review
IBM
IBM watsonx.ai is more enterprise oriented providing more options regarding on-premises setup and other compliance issues. Better suited for the corporate world.
Read full review
Return on Investment
Open Source
  • Since we stopped using Caffe before it can reach the production phase, there is no clear ROI that can be defined.
Read full review
IBM
  • We have already met our objectives in creating a supportive environment.
  • This open-source tool increases the financial feasibility of the workflow.
  • High price.
Read full review
ScreenShots

IBM watsonx.ai Screenshots

Screenshot of 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 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 Tuning Studio in watsonx.ai where AI builders can tune foundation models with labeled data for better performance and accuracy.Screenshot of 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.