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Amazon SageMaker vs Google Vertex AI comparison

 

Comparison Buyer's Guide

Executive SummaryUpdated on Oct 8, 2024
 

Categories and Ranking

Amazon SageMaker
Ranking in AI Development Platforms
4th
Average Rating
7.8
Reviews Sentiment
9.1
Number of Reviews
29
Ranking in other categories
Data Science Platforms (5th)
Google Vertex AI
Ranking in AI Development Platforms
3rd
Average Rating
8.4
Number of Reviews
10
Ranking in other categories
AI Infrastructure (1st)
 

Mindshare comparison

As of November 2024, in the AI Development Platforms category, the mindshare of Amazon SageMaker is 8.1%, down from 8.8% compared to the previous year. The mindshare of Google Vertex AI is 23.8%, up from 15.9% compared to the previous year. It is calculated based on PeerSpot user engagement data.
AI Development Platforms
 

Featured Reviews

Natu Lauchande - PeerSpot reviewer
Feb 27, 2024
Easy to use and manage, but the documentation does not have a lot of information
We use the product for deploying machine learning models. We use it for the machine learning model development process We're currently implementing a project on a cross-selling model. It is like a standard XGBoost model. I’m evaluating the tool to see whether it will improve the workflow.…
Serge Dahdouh - PeerSpot reviewer
Aug 16, 2023
A user-friendly platform that automatizes machine learning techniques with minimal effort
I mostly use LLM models on Vertex AI. When there is a large document or multiple documents, I put them in the index database of Vertex AI's platform and it extracts the right information We work with clients who request the implementation of a certain document into a chatbot. Because of the…

Quotes from Members

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

Pros

"The feature I found most valuable is the data catalog, as it assists with the lineage of data through the preparation pipeline."
"The product aggregates everything we need to build and deploy machine learning models in one place."
"The most valuable feature of Amazon SageMaker is SageMaker Studio."
"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 most valuable feature of Amazon SageMaker for me is the model deployment service."
"The tool makes our ML model development a bit more efficient because everything is in one environment."
"I have contacted the solution's technical support, and they were really good. I rate the technical support a ten out of ten."
"SageMaker offers functionalities like Jupyter Notebooks for development, built-in algorithms, model tuning, and options to deploy models on managed infrastructure."
"The monitoring feature is a true life-saver for data scientists. I give it a ten out of ten."
"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."
"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 integration of AutoML features streamlines our machine-learning workflows."
"Google Vertex AI is an out-of-the-box and very easy-to-use solution."
 

Cons

"The platform could be more accessible to users with basic coding skills, making it more intuitive and easier for beginners to use comfortably."
"While integration is available, there are concerns about how secure this integration is, particularly when exposing data to SageMaker."
"Scalability to handle big data can be improved by making integration with networks such as Hadoop and Apache Spark easier."
"One area where Amazon SageMaker could improve is its pricing. The high costs can drive companies to explore other cloud options. Additionally, while generally good, the updates sometimes come with bugs, and the documentation could be much better. More examples and clearer guidance would be helpful."
"When starting a new session, the waiting time can be quite long, ranging from two to five minutes."
"Lacking in some machine learning pipelines."
"AI is a new area and AWS needs to have an internship training program available."
"The solution is complex to use."
"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."
"Google Vertex AI is good in machine learning and AI, but it lacks optimization."
"I'm not sure if I have suggestions for improvement."
"Both major systems, Azure and Google, are not yet stabilized, especially their customer support."
"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."
"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."
 

Pricing and Cost Advice

"The pricing is comparable."
"The support costs are 10% of the Amazon fees and it comes by default."
"I would rate the solution's price a ten out of ten since it is very high."
"The tool's pricing is reasonable."
"There is no license required for the solution since you can use it on demand."
"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 pricing is complicated as it is based on what kind of machines you are using, the type of storage, and the kind of computation."
"The cost offers a pay-as-you-go pricing model. It depends on the instance that you do."
"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 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"
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Top Industries

By visitors reading reviews
Financial Services Firm
18%
Educational Organization
14%
Computer Software Company
11%
Manufacturing Company
8%
Financial Services Firm
13%
Computer Software Company
13%
Manufacturing Company
9%
Retailer
7%
 

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...
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?
The license cost for Amazon SageMaker ranges between seven thousand to fifteen thousand dollars per month depending on various factors such as the model, amount of data, and geographical locations ...
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?
Google Vertex AI is relatively expensive, rating it six for pricing. The pricing model could be more flexible.
What needs improvement with Google Vertex AI?
Both major systems, Azure and Google, are not yet stabilized, especially their customer support. It is essential to have a robust customer support system to get quick resolutions as they're frequen...
 

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: October 2024.
815,854 professionals have used our research since 2012.