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Amazon SageMaker vs Google Vertex AI comparison

 

Comparison Buyer's Guide

Executive SummaryUpdated on Jul 27, 2025

Review summaries and opinions

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
 

Categories and Ranking

Amazon SageMaker
Ranking in AI Development Platforms
5th
Average Rating
7.8
Reviews Sentiment
7.0
Number of Reviews
38
Ranking in other categories
Data Science Platforms (2nd)
Google Vertex AI
Ranking in AI Development Platforms
2nd
Average Rating
8.4
Reviews Sentiment
6.7
Number of Reviews
12
Ranking in other categories
AI Infrastructure (1st)
 

Mindshare comparison

As of September 2025, in the AI Development Platforms category, the mindshare of Amazon SageMaker is 4.8%, down from 7.2% compared to the previous year. The mindshare of Google Vertex AI is 10.6%, down from 20.5% compared to the previous year. It is calculated based on PeerSpot user engagement data.
AI Development Platforms Market Share Distribution
ProductMarket Share (%)
Google Vertex AI10.6%
Amazon SageMaker4.8%
Other84.6%
AI Development Platforms
 

Featured Reviews

Saurabh Jaiswal - PeerSpot reviewer
Create innovative assistants with seamless data integration for large-scale projects
The various integration options available in Amazon SageMaker, such as Firehose for connecting to data pipelines, are simple to use. Tools like AWS Glue integrate well for data transformations. The Databricks integration aids data scientists and engineers. SageMaker is fully managed, offers high availability, flexibility with TensorFlow, PyTorch, and MXNet, and comes with pre-trained algorithms for forecasting, anomaly detection, and more.
Hamada Farag - PeerSpot reviewer
Customization and integration empower diverse AI applications
We are familiar with most Google Cloud services, particularly infrastructure services, storage, compute, AI tools, containerization, GCP containerization, and cloud SQL. We are familiar with approximately eighty percent of Google's services, primarily related to infrastructure, AI, containers, backup, storage, and compute. We are familiar with Gemini AI and Google Vertex AI, and we have completed some exercises and cases with our customers for Google AI. We use automation in machine learning. I work with a team where everyone has specific responsibilities. We have design and development processes in place. Based on my experience, I would rate Google Vertex AI a 9 out of 10.

Quotes from Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
 

Pros

"Amazon SageMaker is highly valuable for managing ML workloads. It connects to AWS cloud resources, making it easy to deploy algorithms and collaborate using tools like GitLab. It offers a wide range of Python libraries and other necessary tools for modelling and algorithms."
"The superb thing that SageMaker brings is that it wraps everything well. It's got the deployment, the whole framework."
"The tool has made client management easier where patients need to upload their health records and we can use the tool to understand details on treatment date, amount, etc."
"SageMaker is a comprehensive platform where I can perform all machine learning activities."
"We've had experience with unique ML projects using SageMaker. For example, we're developing a platform similar to ChatGPT that requires models. We utilize Amazon SageMaker to create endpoints for these models, making accessing them convenient as needed."
"The intuitive interface and streamlined user experience make it easy to navigate and set up various tools like Visual Studio Code or Jupyter Notebook."
"The most valuable features in Amazon SageMaker are its AutoML, feature store, and automated hyperparameter tuning capabilities."
"The technical support from AWS is excellent."
"We extensively utilize Google Cloud's Vertex AI platform for our machine learning workflows. Specifically, we leverage the IO branch for EDA data in Suresh Live Virtual, employing Forte IT for training machine learning models. The AI model registry in Vertex AI is crucial for cataloging and managing various versions of the models we develop. When it comes to deploying models, we rely on Google Cloud's AI Prediction service, seamlessly integrating it into our workflow for real-time predictions or streaming. For monitoring and tracking the outcomes of model development, we employ Vertex AI Monitoring, ensuring a comprehensive understanding of the model's performance and results. This integrated approach within Vertex AI provides a unified platform for managing, deploying, and monitoring machine learning models efficiently."
"Vertex AI possesses multiple libraries, so it eliminates the need for extensive coding."
"Google Vertex AI is an out-of-the-box and very easy-to-use solution."
"The support is perfect and fantastic."
"The monitoring feature is a true life-saver for data scientists. I give it a ten out of ten."
"It provides the most valuable external analytics."
"The integration of AutoML features streamlines our machine-learning workflows."
"Vertex comes with inbuilt integration with GCP for data storage."
 

Cons

"The training modules could be enhanced. We had to take in-person training to fully understand SageMaker, and while the trainers were great, I think more comprehensive online modules would be helpful."
"I would say the IDE is quite immature, but it is still in its infancy, so I expect it to get better over time."
"The solution is complex to use."
"In my opinion, one improvement for Amazon SageMaker would be to offer serverless GPUs. Currently, we incur costs on an hourly basis. It would be beneficial if the tool could provide pay-as-you-go pricing based on endpoints."
"The entry point can be a bit difficult. Having all documentation easily accessible on the front page of SageMaker would be a great improvement."
"The platform could be more accessible to users with basic coding skills, making it more intuitive and easier for beginners to use comfortably."
"In general, improvements are needed on the performance side of the product's graphical user interface-related area since it consumes a lot of time for a user."
"When starting a new session, the waiting time can be quite long, ranging from two to five minutes."
"I believe that Vertex AI is a robust platform, but its effectiveness depends significantly on the domain knowledge of the developer using it. While Vertex AI does offer support through the console UI in the Google Cloud environment, it is better suited for technical members who have a deeper understanding of machine learning concepts. The platform may be challenging for business process developers (BPDUs) who lack extensive technical knowledge, as it involves intricate customization and handling numerous parameters. Effectively utilizing Vertex AI requires not only familiarity with machine learning frameworks like TensorFlow or PyTorch but also a proficiency in Python programming. The complexity of these requirements might pose challenges for less technically oriented users, making it crucial to have a solid foundation in both machine learning principles and Python coding to extract the full value from Vertex AI. It would be beneficial to have a streamlined process where we can leverage the capabilities of Vertex AI directly through the BigQuery UI. This could involve functionalities such as creating machine learning models within the BigQuery UI, providing a more user-friendly and integrated experience. This would allow users to access and analyze data from BigQuery while simultaneously utilizing Vertex AI to build machine learning models, fostering a more cohesive and efficient workflow."
"I've noticed that using chat activity often presents a broader range of options and insights for a well-constructed question. Improving the knowledge base could be a key aspect for enhancement—expanding the information sources to enhance the generation process."
"It would be beneficial to have certain features included in the future, such as image generators and text-to-speech solutions."
"Google Vertex AI is good in machine learning and AI, but it lacks optimization."
"The tool's documentation is not good. It is hard."
"I'm not sure if I have suggestions for improvement."
"The solution is stable, but it is quite slow. Maybe my data is too large, but I think that Google could improve Vertex AI's training time."
"It is not completely mature and needs some features and functions. The interface needs to be more user-friendly."
 

Pricing and Cost Advice

"The pricing is comparable."
"The tool's pricing is reasonable."
"I rate the pricing a five on a scale of one to ten, where one is the lowest price, and ten is the highest price. The solution is priced reasonably. There is no additional cost to be paid in excess of the standard licensing fees."
"There is no license required for the solution since you can use it on demand."
"The pricing could be better, especially for querying. The per-query model feels expensive."
"The support costs are 10% of the Amazon fees and it comes by default."
"On average, customers pay about $300,000 USD per month."
"You don't pay for Sagemaker. You only pay for the compute instances in your storage."
"The Versa AI offers attractive pricing. With this pricing structure, I can leverage various opportunities to bring value to my business. It's a positive aspect worth considering."
"The price structure is very clear"
"The solution's pricing is moderate."
"I think almost every tool offers a decent discount. In terms of credits or other stuff, every cloud provider provides a good number of incentives to onboard new clients."
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Top Industries

By visitors reading reviews
Financial Services Firm
18%
Computer Software Company
11%
Manufacturing Company
9%
University
6%
Computer Software Company
14%
Financial Services Firm
11%
Manufacturing Company
9%
Educational Organization
6%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business12
Midsize Enterprise11
Large Enterprise16
By reviewers
Company SizeCount
Small Business4
Midsize Enterprise3
Large Enterprise6
 

Questions from the Community

How would you compare Databricks vs Amazon SageMaker?
We researched AWS SageMaker, but in the end, we chose Databricks. Databricks is a Unified Analytics Platform designed to accelerate innovation projects. It is based on Spark so it is very fast. It...
What do you like most about Amazon SageMaker?
We've had experience with unique ML projects using SageMaker. For example, we're developing a platform similar to ChatGPT that requires models. We utilize Amazon SageMaker to create endpoints for t...
What is your experience regarding pricing and costs for Amazon SageMaker?
If you manage it effectively, their pricing is reasonable. It's similar to anything in the cloud; if you don't manage it properly, it can be expensive, but if you do, it's fine.
What do you like most about Google Vertex AI?
We extensively utilize Google Cloud's Vertex AI platform for our machine learning workflows. Specifically, we leverage the IO branch for EDA data in Suresh Live Virtual, employing Forte IT for trai...
What is your experience regarding pricing and costs for Google Vertex AI?
They have different pricing models like pay-as-you-go or subscription model, and total cost of ownership. It is comparatively cheaper than Azure.
What needs improvement with Google Vertex AI?
Google Vertex AI is one of the best in the market, followed by Azure AI. It can be rated at eight or nine out of ten. It is not completely mature and needs some features and functions. The interfac...
 

Also Known As

AWS SageMaker, SageMaker
No data available
 

Overview

 

Sample Customers

DigitalGlobe, Thomson Reuters Center for AI and Cognitive Computing, Hotels.com, GE Healthcare, Tinder, Intuit
Information Not Available
Find out what your peers are saying about Amazon SageMaker vs. Google Vertex AI and other solutions. Updated: July 2025.
867,349 professionals have used our research since 2012.