Vertex AI vs. IBM watsonx.ai

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Vertex AI
Score 8.6 out of 10
N/A
Vertex AI on Google Cloud is an MLOps solution, used to build, deploy, and scale machine learning (ML) models with fully managed ML tools for any use case.
$0
Starting at
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
Vertex AIIBM watsonx.ai
Editions & Modules
Imagen model for image generation
$0.0001
Starting at
Text, chat, and code generation
$0.0001
per 1,000 characters
Text data upload, training, deployment, prediction
$0.05
per hour
Video data training and prediction
$0.462
per node hour
Image data training, deployment, and prediction
$1.375
per node hour
Essentials
$0
per month
Free Trial
$0
per month
Standard
$1,500
per month
Offerings
Pricing Offerings
Vertex AIIBM watsonx.ai
Free Trial
YesYes
Free/Freemium Version
YesYes
Premium Consulting/Integration Services
NoYes
Entry-level Setup FeeOptionalNo setup fee
Additional DetailsPricing is based on the Vertex AI tools and services, storage, compute, and Google Cloud resources used.Get 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
Best Alternatives
Vertex AIIBM watsonx.ai
Small Businesses
Google Cloud AI
Google Cloud AI
Score 8.4 out of 10
IBM SPSS Modeler
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Score 7.8 out of 10
Medium-sized Companies
Google Cloud AI
Google Cloud AI
Score 8.4 out of 10
Posit
Posit
Score 9.1 out of 10
Enterprises
Dataiku
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Score 8.6 out of 10
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Score 7.8 out of 10
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User Ratings
Vertex AIIBM watsonx.ai
Likelihood to Recommend
8.1
(7 ratings)
7.7
(4 ratings)
Performance
8.1
(3 ratings)
-
(0 ratings)
Configurability
8.6
(4 ratings)
-
(0 ratings)
User Testimonials
Vertex AIIBM watsonx.ai
Likelihood to Recommend
Google
We needed to build some ML models and Vertex AI is one such place where all the functionalities are consolidated for model deployment, tweaking and monitoring. And although it is costly, it is compatible with google cloud services and is scalable which makes it the perfect tool for us.
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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.
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Pros
Google
  • Vertex AI comes with support for LOTs of LLMs out of the box
  • MLOps tools are available that help to standardize operational aspects
  • Document AI is an out of the box feature that works just perfectly for our use cases of automating lots to tedious data extraction tasks from images as well as papers
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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.
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Cons
Google
  • Customization of AutoML models - A must needed capability to be able to tweak hyperparameters and also working with different models
  • Model Explainability -Providing more comprehensive explanations about how models are utilizing features could be very beneficial
  • Model versioning and experiments tracking - Enhancing the versioning capability could be good for end users
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IBM
  • APIs integration could be improved.
  • steep learnings for tuning AI models
  • performance lag
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Performance
Google
Vertex AI is very user friendly and it is very fast. We can spin up instances pretty quickly and it allows to work with GPU instances without much planning. Pages load up very quickly and tasks are competed pretty much in time frame. It integrates with other systems also very well.
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IBM
No answers on this topic
Alternatives Considered
Google
Vertex AI is much more accessible to non-developers than IBM's product. Moreover, Vertex AI integrates well with other Google products, enhancing its capabilities. A big plus is its integration with cloud storage, that allows for better management and access of data. In all honesty, it wasn't much of a difficult choice to choose Vertex AI.
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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.
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Return on Investment
Google
  • It consolidated all the functionalities in one place for AI and ML development.
  • Scalable and compatible with google cloud services.
  • Though we can manually allocate resources, it is still very costly.
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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.
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ScreenShots

Vertex AI Screenshots

Screenshot of an introduction to generative AI on Vertex AI - Vertex AI Studio offers a Google Cloud console tool for rapidly prototyping and testing generative AI models.Screenshot of gen AI for summarization, classification, and extraction - Text prompts can be created to handle any number of tasks with Vertex AI’s generative AI support. Some of the most common tasks are classification, summarization, and extraction. Vertex AI’s PaLM API for text can be used to design prompts with flexibility in terms of their structure and format.Screenshot of Custom ML training overview and documentation - An overview of the custom training workflow in Vertex AI, the benefits of custom training, and the various training options that are available. This page also details every step involved in the ML training workflow from preparing data to predictions.Screenshot of ML model training and creation -  A guide that shows how Vertex AI’s AutoML is used to create and train custom machine learning models with minimal effort and machine learning expertise.Screenshot of deployment for batch or online predictions - When using a model to solve a real-world problem, the Vertex AI prediction service can be used for batch and online predictions.

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.