We compared Amazon SageMaker and Google Vertex AI based on our user's reviews in several parameters.
In summary, Amazon SageMaker is praised for its ease of use, comprehensive machine learning capabilities, and efficient customer service, although some users see areas for improvement. Google Vertex AI stands out for its advanced machine learning capabilities, efficient model training, reliable customer service, and positive ROI, yet users note room for improvement in customization options and support responsiveness.
Features: Amazon SageMaker offers valuable features such as comprehensive ML capabilities, customizable workflows, and robust monitoring tools. Google Vertex AI stands out with advanced ML capabilities, seamless integration, efficient training processes, user-friendly interface, and scalability for all project sizes.
Pricing and ROI: The setup cost for Amazon SageMaker is reported to be straightforward and hassle-free, with a clear and transparent licensing process. On the other hand, Google Vertex AI's setup process is also straightforward and hassle-free, requiring minimal effort. Its licensing is praised for being flexible and accommodating to different business needs., Users have reported positive ROI with both Amazon SageMaker and Google Vertex AI. Amazon SageMaker is praised for delivering value and benefits, while Google Vertex AI is commended for enhancing productivity, optimizing processes, and providing cost-effectiveness and innovative features.
Room for Improvement: Amazon SageMaker: Users have identified areas where Amazon SageMaker could be enhanced. Google Vertex AI: Some users mentioned the need for better customization options and more comprehensive documentation. They also highlighted the need for enhanced support and responsiveness from the customer service team.
Deployment and customer support: The feedback for Amazon SageMaker and Google Vertex AI reveals that users had varying experiences in terms of the duration required for establishing a new tech solution. Some users reported spending three months on deployment and an additional week on setup for both products. The context needs to be carefully evaluated to determine if the terms refer to the same period or separate phases., Amazon SageMaker's customer service is praised for its helpfulness, responsiveness, efficiency, knowledge, and prompt resolution of issues. Users appreciate the team's attentiveness and commitment. On the other hand, Google Vertex AI's customer service is commendable, reliable, prompt, professional, and effective. Users express contentment with their assistance and guidance.
The summary above is based on 14 interviews we conducted recently with Amazon SageMaker and Google Vertex AI users. To access the review's full transcripts, download our report.
"The few projects we have done have been promising."
"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 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 Autopilot feature is really good because it's helpful for people who don't have much experience with coding or data pipelines. When we suggest SageMaker to clients, they don't have to go through all the steps manually. They can leverage Autopilot to choose variables, run experiments, and monitor costs. The results are also pretty accurate."
"The tool makes our ML model development a bit more efficient because everything is in one environment."
"Allows you to create API endpoints."
"The most valuable feature of Amazon SageMaker is its integration. For example, AWS Lambda. Additionally, we can write Python code."
"The monitoring feature is a true life-saver for data scientists. I give it a ten out of ten."
"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."
"It provides the most valuable external analytics."
"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 documentation must be made clearer and more user-friendly."
"Scalability to handle big data can be improved by making integration with networks such as Hadoop and Apache Spark easier."
"The product must provide better documentation."
"There are other better solutions for large data, such as Databricks."
"Lacking in some machine learning pipelines."
"The pricing of the solution is an issue...In SageMaker, monitoring could be improved by supporting more data types other than JSON and CSV."
"The solution is complex to use."
"Creating notebook instances for multiple users is pretty expensive in Amazon SageMaker."
"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 Vertex AI is good in machine learning and AI, but it lacks optimization."
"It would be beneficial to have certain features included in the future, such as image generators and text-to-speech solutions."
"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."
Amazon SageMaker is ranked 5th in AI Development Platforms with 18 reviews while Google Vertex AI is ranked 3rd in AI Development Platforms with 5 reviews. Amazon SageMaker is rated 7.2, while Google Vertex AI is rated 8.4. The top reviewer of Amazon SageMaker writes "Easy to use and manage, but the documentation does not have a lot of information". On the other hand, the top reviewer of Google Vertex AI writes "A user-friendly platform that automatizes machine learning techniques with minimal effort". Amazon SageMaker is most compared with Databricks, Azure OpenAI, Domino Data Science Platform, Microsoft Azure Machine Learning Studio and Dataiku Data Science Studio, whereas Google Vertex AI is most compared with Azure OpenAI, Microsoft Azure Machine Learning Studio, Hugging Face, TensorFlow and AWS Machine Learning. See our Amazon SageMaker vs. Google Vertex AI report.
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