Amazon SageMaker vs Google Vertex AI comparison

Amazon Web Services (AWS) and Google are both solutions in the AI Development Platforms category. Amazon Web Services (AWS) is ranked #5 with an average rating of 8.0, while Google is ranked #2 with an average rating of 8.3. Amazon Web Services (AWS) holds a 5.6% mindshare in AIDP, compared to Google’s 14.0% mindshare. Additionally, 91% of Amazon Web Services (AWS) users are willing to recommend the solution, compared to 100% of Google users who would recommend it.

Amazon SageMaker

Read 36 Amazon SageMaker reviews

4,140 Views
3,302 Comparison Views

91% willing to recommend

Google Vertex AI

Read 10 Google Vertex AI reviews

8,938 Views
8,024 Comparison Views

100% willing to recommend

Amazon SageMaker

Google Vertex AI

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Executive SummaryUpdated on Apr 20, 2025

Amazon SageMaker and Google Vertex AI compete in the machine learning and AI platform category. Google Vertex AI appears to have the upper hand due to its user-friendliness and effective integration with Google Cloud Platform.

Features: Amazon SageMaker offers integrated development environments, pre-built solutions like Random Cut Forest, and ease of use through features like Canvas and Autopilot. SageMaker's integration with AWS services helps simplify deployment for AWS Cloud users. Google Vertex AI stands out with its Feature Store, model monitoring, and AutoML features, enabling simplified machine learning model deployment without a large team.

Room for Improvement: Amazon SageMaker users note complexities and high costs, recommending improved documentation, simplified pricing, and better support for large data integration. Suggestions include free trials and enhanced IDE features. Google Vertex AI could improve documentation, user-friendliness, and offer more advanced settings and flexibility for users with basic coding skills.

Ease of Deployment and Customer Service: Amazon SageMaker supports deployments mainly on the public cloud with options for private and hybrid models, though some users report slow technical support. Google Vertex AI is praised for efficient public cloud deployment and effective support, though some find the platform's complexity challenging.

Pricing and ROI: Amazon SageMaker's pricing can be complex and expensive, especially for large instances, though it offers flexibility through a pay-as-you-go model. Despite high costs, it provides significant ROI via efficiency gains. Google Vertex AI features transparent and flexible pricing, making it cost-effective for machine learning applications with significant ROI potential through Google Cloud’s competitive pricing model.

To learn more, read our detailed Amazon SageMaker vs. Google Vertex AI Report (Updated: April 2025).

Buyer's Guide

Amazon SageMaker vs. Google Vertex AI

April 2025

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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

7.1

Number of Reviews

Ranking in other categories

Data Science Platforms (3rd)

Google Vertex AI

Ranking in AI Development Platforms

2nd

Average Rating

8.4

Reviews Sentiment

7.4

Number of Reviews

Ranking in other categories

AI Infrastructure (1st)

Mindshare comparison

As of April 2025, in the AI Development Platforms category, the mindshare of Amazon SageMaker is 5.6%, down from 8.6% compared to the previous year. The mindshare of Google Vertex AI is 14.0%, down from 20.3% compared to the previous year. It is calculated based on PeerSpot user engagement data.

AI Development Platforms

Featured Reviews

Hemant Paralkar

Lead Consultant at Saama

Improves team collaboration with advanced feature sharing but needs a better user experience

Improvement is needed in the no-code and low-code capabilities of Amazon SageMaker. This would empower citizen data scientists to utilize the tool more effectively since many data scientists do not have a core development background. Additionally, dealing with frequent UI updates can be challenging, especially for infrastructure architects like myself. It involves effort to migrate to new UIs, making the updates not seamless. User auditing requires enhancements as tracking operations performed by users can be difficult due to dynamic IP validation and role propagation.

Read full review

Serge Dahdouh

CEO at Les Roches International School of Hotel Management

A user-friendly platform that automatizes machine learning techniques with minimal effort

We work with clients who request the implementation of a certain document into a chatbot. Because of the limited knowledge of AI, our task is to link that file to the ML and provide a platform that can work as a customer service. We previously used LangChain Phython, but now it is done through Vertex AI.

Read full review

Quotes from Members

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

Pros

"The evolution from SageMaker Classic to SageMaker Studio, particularly the UI part of Studio, is commendable."

"The product aggregates everything we need to build and deploy machine learning models in one place."

"The most valuable feature of Amazon SageMaker is its integration. For example, AWS Lambda. Additionally, we can write Python code."

"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."

"The support is very good with well-trained engineers whose training curriculum is rigorous."

"I appreciate the ease of use in Amazon SageMaker."

"The solution's ability to improve work at my organization stems from the ensemble model and a combination of various models it provides."

"I recommend SageMaker for ML projects if you need to build models from scratch."

More Amazon SageMaker pros

"It provides the most valuable external analytics."

"Vertex AI possesses multiple libraries, so it eliminates the need for extensive coding."

"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."

"The most valuable features of the solution are that it is quite flexible, and some of the services are almost low-code, with no-code services, so it gives agents flexibility to build the use cases according to the operational needs."

"Vertex comes with inbuilt integration with GCP for data storage."

"Google Vertex AI is an out-of-the-box and very easy-to-use solution."

"The integration of AutoML features streamlines our machine-learning workflows."

"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."

More Google Vertex AI pros

Cons

"AI is a new area and AWS needs to have an internship training program available."

"The entry point can be a bit difficult. Having all documentation easily accessible on the front page of SageMaker would be a great improvement."

"Improvement is needed in the no-code and low-code capabilities of Amazon SageMaker."

"The user interface (UI) and user experience (UX) of SageMaker and AWS, in general, need improvement as they are not intuitive and require substantial time to learn how to use specific services."

"The product must provide better documentation."

"For any cloud provider, the cost has to be substantially reduced, especially in the case of Amazon SageMaker, which is extremely expensive for huge workloads."

"Having all documentation easily accessible on the front page of SageMaker would be a great improvement."

"Improvement is needed in the no-code and low-code capabilities of Amazon SageMaker. This would empower citizen data scientists to utilize the tool more effectively since many data scientists do not have a core development background."

More Amazon SageMaker cons

"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."

"Both major systems, Azure and Google, are not yet stabilized, especially their customer support."

"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."

"I think the technical documentation is not readily available in the tool."

"The tool's documentation is not good. It is hard."

"It would be beneficial to have certain features included in the future, such as image generators and text-to-speech solutions."

"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."

More Google Vertex AI cons

Pricing and Cost Advice

"The product is expensive."

"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."

"The solution is relatively cheaper."

"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."

"SageMaker is worth the money for our use case."

"You don't pay for Sagemaker. You only pay for the compute instances in your storage."

"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."

More Amazon SageMaker pricing and cost advice

"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."

"The solution's pricing is moderate."

"The price structure is very clear"

"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."

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Top Industries

By visitors reading reviews

Financial Services Firm

19%

Educational Organization

11%

Computer Software Company

11%

Manufacturing Company

Computer Software Company

13%

Financial Services Firm

13%

Manufacturing Company

Retailer

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

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...

See all answers

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...

See all answers

What is your experience regarding pricing and costs for Amazon SageMaker?

Before deploying SageMaker, I reviewed the pricing, especially for notebook instances. The cost for small to medium instances is not very high.

See all answers

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...

See all answers

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.

See all answers

What needs improvement with Google Vertex AI?

I'm not sure if I have suggestions for improvement. Based on my comparison between the two, Vertex has various additional functionalities that Azure doesn't provide.

See all answers

Comparisons

Databricks vs Amazon SageMaker

Compared 17% of the time

Azure OpenAI vs Amazon SageMaker

Compared 12% of the time

Dataiku vs Amazon SageMaker

Compared 11% of the time

Domino Data Science Platform vs Amazon SageMaker

Compared 7% of the time

DataRobot vs Amazon SageMaker

Compared 6% of the time

More Amazon SageMaker Competitors

Azure OpenAI vs Google Vertex AI

Compared 36% of the time

Microsoft Azure Machine Learning Studio vs Google Vertex AI

Compared 20% of the time

Hugging Face vs Google Vertex AI

Compared 11% of the time

TensorFlow vs Google Vertex AI

Compared 4% of the time

IBM Watson Studio vs Google Vertex AI

Compared 3% of the time

More Google Vertex AI Competitors

Product Reports

Buyer's Guide

Amazon SageMaker

April 2025

Download Amazon SageMaker product report

Buyer's Guide

Google Vertex AI

April 2025

Download Google Vertex AI product report

Also Known As

AWS SageMaker, SageMaker

No data available

Overview

Amazon SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. Amazon SageMaker removes all the barriers that typically slow down developers who want to use machine learning.

Amazon Web Services (AWS)

Build, deploy, and scale ML models faster, with pre-trained and custom tooling within a unified artificial intelligence platform.

Google

Sample Customers

DigitalGlobe, Thomson Reuters Center for AI and Cognitive Computing, Hotels.com, GE Healthcare, Tinder, Intuit

Information Not Available

Buyer's Guide

Amazon SageMaker vs. Google Vertex AI

April 2025

Free Report: Amazon SageMaker vs. Google Vertex AI

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