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 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
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
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
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.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
Vertex AIIBM watsonx.ai
Considered Both Products
Vertex AI

No answer on this topic

IBM watsonx.ai
Chose IBM watsonx.ai
To identify IBM watsonx.ai, our team has reviewed other AI choices we met from Google's Vertex AI and AI services provided by OpenAI. Even those offered strong generative capabilities; what was not found in IBM watsonx.ai were the several enterprise attributes that were …
Chose IBM watsonx.ai
I think that the user interface is where IBM watsonx.ai shines the most compared to competitors.
There is a visual tool to build AI pipelines in a very easy and instinctive way, that anybody can master in no time I think.
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Vertex AIIBM watsonx.ai
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User Ratings
Vertex AIIBM watsonx.ai
Likelihood to Recommend
8.0
(13 ratings)
9.0
(33 ratings)
Likelihood to Renew
-
(0 ratings)
6.4
(1 ratings)
Usability
-
(0 ratings)
7.9
(6 ratings)
Performance
7.1
(10 ratings)
-
(0 ratings)
Configurability
7.3
(10 ratings)
-
(0 ratings)
Ease of integration
-
(0 ratings)
6.4
(2 ratings)
Product Scalability
-
(0 ratings)
9.1
(1 ratings)
User Testimonials
Vertex AIIBM watsonx.ai
Likelihood to Recommend
Google
Vertex AI seems to be a lot more accurate with image editing versus other competitors (including free one). We do a lot of image creation, especially of dogs in very certain scenarios. We use Adobe Stock to get us started, but many times we need some very specific edits done to the image. We've found Vertex can produce those with a lot more precision than other AI image generators.
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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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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 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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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
  • 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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Likelihood to Renew
Google
No answers on this topic
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.
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Usability
Google
No answers on this topic
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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Performance
Google
It's not always instant, but understandable when it's under heavy load. It's not impressive nor disappointing, just what is expected. But when calling this platform through API's for it to do the actions requested there is minimal delay and wait time. It feels very responsive and quick when integrating it with a call center chat platform for example.
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IBM
No answers on this topic
Support Rating
Google
No answers on this topic
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
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 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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Scalability
Google
No answers on this topic
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
Return on Investment
Google
  • It is pay as you go model so it'll save more cost of your org. In our case previously we used to incurred 1-2L/Month now we are reduced it to 80k-1L.
  • It'll help you save your model training & model selection time as it provides pre-trained models in autoML.
  • It'll help you in terms of Security wherein we can use row level security access to authorized persons.
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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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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 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.