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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
4th
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
8.2
Reviews Sentiment
6.4
Number of Reviews
14
Ranking in other categories
AI-Agent Builders (4th)
 

Mindshare comparison

As of October 2025, in the AI Development Platforms category, the mindshare of Amazon SageMaker is 4.9%, down from 7.0% compared to the previous year. The mindshare of Google Vertex AI is 10.3%, down from 19.5% compared to the previous year. It is calculated based on PeerSpot user engagement data.
AI Development Platforms Market Share Distribution
ProductMarket Share (%)
Google Vertex AI10.3%
Amazon SageMaker4.9%
Other84.8%
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

"The various integration options available in Amazon SageMaker, such as Firehose for connecting to data pipelines, are simple to use."
"The most valuable feature of Amazon SageMaker is that you don't have to do any programming in order to perform some of your use cases."
"The product aggregates everything we need to build and deploy machine learning models in one place."
"Feature Store, CodeCommit, versioning, model control, and CI/CD pipelines are the most valuable features in Amazon SageMaker."
"We were able to use the product to automate processes."
"I have seen a return on investment, probably a factor of four or five."
"I have contacted the solution's technical support, and they were really good. I rate the technical support a ten out of ten."
"I recommend SageMaker for ML projects if you need to build models from scratch."
"Vertex comes with inbuilt integration with GCP for data storage."
"The most valuable feature we've found is the model garden, which allows us to deploy and use various models through the provided endpoints easily."
"With just one single platform, Google Vertex AI platform, we can achieve everything; we need not switch over to multiple tools, multiple platforms, as everything can be accomplished through this one single platform for integration with existing workflows, systems, tools, and databases."
"The monitoring feature is a true life-saver for data scientists. I give it a ten out of ten."
"The support is perfect and fantastic."
"Vertex AI possesses multiple libraries, so it eliminates the need for extensive coding."
"The most useful function of Google Vertex AI for me is the ease of integration, as we can easily create a prompt and integrate it into our current system."
"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."
 

Cons

"Amazon SageMaker could improve in the area of hyperparameter tuning by offering more automated suggestions and tips during the tuning process."
"I would say the IDE is quite immature, but it is still in its infancy, so I expect it to get better over time."
"Improvements are needed in terms of complexity, data security, and access policy integration in Amazon SageMaker."
"Improvement is needed in the no-code and low-code capabilities of Amazon SageMaker."
"The documentation must be made clearer and more user-friendly."
"The main challenge with Amazon SageMaker is the integrations."
"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."
"The solution needs to be cheaper since it now charges per document for extraction."
"It is not completely mature and needs some features and functions. The interface needs to be more user-friendly."
"The tool's documentation is not good. It is hard."
"We used AutoML feature for developing AI models automatically, but we are not comfortable with the performance of those models."
"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."
"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."
"Google can improve Google Vertex AI in terms of analysis and accuracy. When passing a very large context, instead of receiving vague responses, it would be better if the system could prompt users not to pass overly large prompts and provide clearer guidance on how to fine-tune Gemini for specific use cases."
"It takes a considerable amount of time to process, and I understand the technology behind why it takes this long, but this is something that could be reduced."
"Google Vertex AI is good in machine learning and AI, but it lacks optimization."
 

Pricing and Cost Advice

"Databricks solution is less costly than Amazon SageMaker."
"The cost offers a pay-as-you-go pricing model. It depends on the instance that you do."
"The support costs are 10% of the Amazon fees and it comes by default."
"The pricing is complicated as it is based on what kind of machines you are using, the type of storage, and the kind of computation."
"There is no license required for the solution since you can use it on demand."
"You don't pay for Sagemaker. You only pay for the compute instances in your storage."
"SageMaker is worth the money for our use case."
"On a scale from one to ten, where one is cheap, and ten is expensive, I rate the solution's pricing a six out of ten."
"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 solution's pricing is moderate."
"The price structure is very clear"
"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
8%
University
6%
Computer Software Company
14%
Financial Services Firm
10%
Manufacturing Company
9%
Educational Organization
7%
 

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 Business5
Midsize Enterprise3
Large Enterprise7
 

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?
We used AutoML feature for developing AI models automatically, but we are not comfortable with the performance of those models. We have to do some fine-tuning, hyperparameter optimization, and othe...
 

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: September 2025.
872,706 professionals have used our research since 2012.