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."
"Allows you to create API endpoints."
"The solution's ability to improve work at my organization stems from the ensemble model and a combination of various models it provides."
"The most valuable feature of Amazon SageMaker is its integration. For example, AWS Lambda. Additionally, we can write Python code."
"Feature Store, CodeCommit, versioning, model control, and CI/CD pipelines are the most valuable features in Amazon SageMaker."
"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 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 product aggregates everything we need to build and deploy machine learning models in one place."
"Azure OpenAI is very easy to use instead of AWS services."
"The high precision of information extraction is the most valuable feature."
"It is easy to integrate and develop a solution. Most customers are concerned about the security of their data and how cost-effective it is. We have developed some methodologies so that our customers will not be charged too much for these OpenAI services but will still get the same kind of performance and results. It's all developed on Azure, so customers also see its benefit."
"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."
"OpenAI's models are more mature than Watson's. They offer a wider range of features and provide richer outputs."
"Azure OpenAI is useful for benchmarking products."
"It's very powerful. It allows users to query our documents using natural language and receive answers in the same way. This makes our product information much more accessible than traditional keyword-based search."
"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."
"Scalability to handle big data can be improved by making integration with networks such as Hadoop and Apache Spark easier."
"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."
"In my opinion, one improvement for Amazon SageMaker would be to offer serverless GPUs. Currently, we incur costs on an hourly basis. It would be beneficial if the tool could provide pay-as-you-go pricing based on endpoints."
"AI is a new area and AWS needs to have an internship training program available."
"SageMaker would be improved with the addition of reporting services."
"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 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."
"There are other better solutions for large data, such as Databricks."
"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."
"Azure OpenAI should use more specific sources like academic articles because sometimes the source can't be found."
"We are awaiting the new updates like multi-model capabilities."
"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."
"Sometimes, the responses are repetitive."
"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 faced one issue with Azure OpenAI: My customer wanted more clarity on the pricing. They were not able to get proper answers from the documentation or the pricing calculator. I suggest that Microsoft maintain standardization in the pricing details published in the documentation and the pricing calculator."
"Deployment was slightly complex for me to understand."
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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