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 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 solution's ability to improve work at my organization stems from the ensemble model and a combination of various models it provides."
"The few projects we have done have been promising."
"I have contacted the solution's technical support, and they were really good. I rate the technical support a ten out of ten."
"Allows you to create API endpoints."
"The tool makes our ML model development a bit more efficient because everything is in one environment."
"The most valuable feature of Amazon SageMaker for me is the model deployment service."
"Feature Store, CodeCommit, versioning, model control, and CI/CD pipelines are the most valuable features in Amazon SageMaker."
"Azure OpenAI is very easy to use instead of AWS services."
"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."
"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."
"The product's initial setup phase was pretty easy."
"The product saves a lot of time."
"OpenAI's models are more mature than Watson's. They offer a wider range of features and provide richer outputs."
"You just have to write accurate prompts according to your requirements, and the solution gives very good results."
"The product is easy to integrate with our IT workflow."
"There are other better solutions for large data, such as Databricks."
"AI is a new area and AWS needs to have an internship training program available."
"Scalability to handle big data can be improved by making integration with networks such as Hadoop and Apache Spark easier."
"The payment and monitoring metrics are a bit confusing not only for Amazon SageMaker but also for the range of other products that fall under AWS, especially for a new user of the product."
"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."
"SageMaker would be improved with the addition of reporting services."
"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."
"Lacking in some machine learning pipelines."
"There is room for improvement in their support services."
"We are awaiting the new updates like multi-model capabilities."
"There are no available updates of information that are currently provided."
"Azure OpenAI is not an optimized tool yet, making it one of its shortcomings where improvements are required."
"Azure OpenAI is not available in all regions, and its technical support should be improved."
"We encountered challenges related to question understanding."
"Since we don't train the model on our data, it's a struggle to ensure OpenAI answers questions exclusively from our data. During user testing, we found ways to make the system provide answers from outside sources."
"I have found the tool unreliable in certain use cases. I aim to enhance the system's latency, particularly in responding to calls. Occasionally, calls don't respond, so I want to improve reliability."
Amazon SageMaker is ranked 5th in AI Development Platforms with 19 reviews while Azure OpenAI is ranked 2nd in AI Development Platforms with 24 reviews. Amazon SageMaker is rated 7.4, 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, Dataiku and DataRobot, 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.