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.
"I have contacted the solution's technical support, and they were really good. I rate the technical support a ten out of ten."
"The superb thing that SageMaker brings is that it wraps everything well. It's got the deployment, the whole framework."
"The product aggregates everything we need to build and deploy machine learning models in one place."
"Allows you to create API endpoints."
"The deployment is very good, where you only need to press a few buttons."
"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 solution is easy to scale...The documentation and online community support have been sufficient for us so far."
"The tool makes our ML model development a bit more efficient because everything is in one environment."
"The most crucial aspect is the conversational capability, where you can simply ask questions, and it provides answers based on your content and documents, particularly tailored to your specific environment."
"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."
"Our clients are interested in building knowledge bases, particularly in child welfare. In this domain, we focus on supporting caseworkers by compiling and organizing relevant information. This information is then stored in a database using a query. The database generates summaries and reminders for specific actions and even facilitates sending emails to parents or other relevant parties. The system's complexity is tailored to the specific needs of child welfare cases. Additionally, we're exploring opportunities to assist a healthcare organization. Specifically, we're working on streamlining the process of filling out forms required for insurance claims. This effort aims to ensure that hospitals can receive funding or payment for the care they provide."
"Azure OpenAI is very easy to use instead of AWS services."
"Generative AI or GenAI seems to be the best part of the solution."
"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 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."
"The product must provide better documentation."
"The pricing of the solution is an issue...In SageMaker, monitoring could be improved by supporting more data types other than JSON and CSV."
"Lacking in some machine learning pipelines."
"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."
"AI is a new area and AWS needs to have an internship training program available."
"The solution needs to be cheaper since it now charges per document for extraction."
"Creating notebook instances for multiple users is pretty expensive in Amazon SageMaker."
"SageMaker would be improved with the addition of reporting services."
"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."
"Sometimes, the responses are repetitive."
"Azure OpenAI is not an optimized tool yet, making it one of its shortcomings where improvements are required."
"The solution needs to accommodate smaller companies."
"There are certain shortcomings with the product's scalability and support team where improvements are required."
"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."
"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."
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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