We compared Amazon SageMaker and Azure OpenAI based on our user's reviews in several parameters.
Amazon SageMaker provides users with efficient model training and deployment, seamless integration with AWS services, and strong customer support. On the other hand, Azure OpenAI offers seamless integration with Azure services, flexible scaling options, and valuable insights for decision-making. Both products receive positive feedback for their pricing, setup process, and ROI, but users have identified areas for improvement.
Features: Amazon SageMaker is highly valued for its ease of use, comprehensive machine learning capabilities, customizable workflows, automated data labeling, and robust monitoring and troubleshooting tools. On the other hand, Azure OpenAI is praised for its seamless integration with Azure services, scalability, robust machine learning capabilities, and excellent documentation and support.
Pricing and ROI: Amazon SageMaker's setup cost is deemed reasonable and straightforward, with clear and transparent licensing. On the other hand, Azure OpenAI is positively regarded for its minimal setup cost, smooth process, and adaptable licensing options, providing cost-efficiency and meeting varying user requirements., Amazon SageMaker has been praised for its positive ROI, providing benefits and value. Azure OpenAI offers increased efficiency and productivity, cost reduction, improved business performance, and valuable insights for decision-making.
Room for Improvement: Users have identified areas where Amazon SageMaker could be enhanced. Many users have provided feedback on ways to enhance Azure OpenAI. They have voiced concerns regarding certain functions and suggested improvements.
Deployment and customer support: Amazon SageMaker: User reviews indicate varying durations for establishing a new tech solution, with some users spending three months on deployment and an additional week on setup, while others mentioned a week for both deployment and setup. Azure OpenAI: Users reported spending three months on deployment and an additional week on setup, suggesting that both timeframes should be considered. Another user required a week for both deployment and setup, indicating that these terms refer to the same period and should not be considered separately., Amazon SageMaker's customer service and support are praised for their helpfulness and responsiveness, efficiency, and promptness in issue resolution. Users appreciate the support team's attentiveness and commitment to addressing customer needs. In comparison, Azure OpenAI's customer service is highly regarded for exceptional assistance, efficient handling of queries, and ensuring a smooth user experience.
The summary above is based on 21 interviews we conducted recently with Amazon SageMaker and Azure OpenAI users. To access the review's full transcripts, download our report.
"The deployment is very good, where you only need to press a few buttons."
"The most valuable feature of Amazon SageMaker is its integration. For example, AWS Lambda. Additionally, we can write Python code."
"The most valuable feature of Amazon SageMaker for me is the model deployment service."
"The product aggregates everything we need to build and deploy machine learning models in one place."
"Allows you to create API endpoints."
"The few projects we have done have been promising."
"The solution's ability to improve work at my organization stems from the ensemble model and a combination of various models it provides."
"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 high precision of information extraction is the most valuable feature."
"The solution has a very drag-and-drop environment. Instead of coding something from scratch or understanding any concept in extensive depth before deployment, this is good. Plus, they have an auto dataset, which means you can choose any dataset they have instead of providing your own. So that's also pretty nice."
"OpenAI's models are more mature than Watson's. They offer a wider range of features and provide richer outputs."
"My goal was to create an experience where project managers don't have to read through entire documents. Instead, they can ask a question and receive relevant point analysis. This analysis identifies the document and specific section where the information resides. Previously, users had to rely on these document references. Now, Azure OpenAI enhances the experience by providing the answer directly in the user's own language, relevant to their context."
"Azure OpenAI is very easy to use instead of AWS services."
"OpenAI integrates seamlessly with the broader Microsoft Azure ecosystem, and that provides synergies with the other solutions. This integration makes it much easier to build solutions."
"The most valuable feature of Azure OpenAI stems from the GPT-3.5 models it provides to its users."
"We can use the solution to implement our tasks and models quickly."
"AI is a new area and AWS needs to have an internship training program available."
"Amazon SageMaker could improve in the area of hyperparameter tuning by offering more automated suggestions and tips during the tuning process."
"Lacking in some machine learning pipelines."
"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."
"There are other better solutions for large data, such as Databricks."
"I would say the IDE is quite immature, but it is still in its infancy, so I expect it to get better over time."
"SageMaker would be improved with the addition of reporting services."
"The solution is complex to use."
"The product must improve its dashboards."
"Sometimes, the responses are repetitive."
"The product features themselves are fine. However, with Microsoft scaling the service so much, the support structure needs to keep pace. When solving complex issues, the process of interacting with Microsoft can be quite time-consuming."
"We are awaiting the new updates like multi-model capabilities."
"I noticed there are no instructional videos or guides on the network portal for initial configurations. There is limited information available, and this is a concern for me. I would like to see more resources and guides to address these issues."
"The fine-tuning of models with the use of Azure OpenAI is an area with certain shortcomings currently, and it can be considered for improvement in the future."
"We encountered challenges related to question understanding."
"There is room for improvement in their support services."
Amazon SageMaker is ranked 5th in AI Development Platforms with 18 reviews while Azure OpenAI is ranked 2nd in AI Development Platforms with 17 reviews. Amazon SageMaker is rated 7.2, while Azure OpenAI is rated 8.0. 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 Azure OpenAI writes "Created a chatbot powered by OpenAI to answer HR, travel, and expense-related questions". Amazon SageMaker is most compared with Databricks, Google Vertex AI, Domino Data Science Platform, Microsoft Azure Machine Learning Studio and Dataiku Data Science Studio, whereas Azure OpenAI is most compared with Google Vertex AI, Microsoft Azure Machine Learning Studio, Hugging Face, Google Cloud AI Platform and IBM Watson Studio. See our Amazon SageMaker vs. Azure OpenAI report.
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We monitor all AI Development Platforms reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.
