AI Showdown: AWS vs. Azure

Artificial intelligence (AI) and machine learning (ML) have become essential tools for businesses looking to stay ahead of the competition. With AI/ML driving innovation across many industries, companies must carefully choose the right cloud platform for their goals. Amazon Web Services (AWS) and Microsoft Azure dominate the cloud space, each with a strong set of AI/ML services. The choice between them can have a big impact on a company’s long-term strategy and success.
In this blog, I’ll broadly compare AI/ML services from AWS and Azure, mostly based on my own experience and perspective as a data and AI architect in a leading professional services company. While I can’t explore every aspect of these platforms, I will concentrate on key areas that have proven to be significant. Ultimately, my goal is to give you a clearer perspective that can help guide your organization’s decision-making process regarding AI/ML adoption.

Machine Learning Platforms

ML platforms like SageMaker and Azure Machine Learning offer comprehensive services, from data prep to building and training models, all the way to deployment and management. You can do data preparation tasks, use popular ML frameworks like TensorFlow or PyTorch, deploy inference endpoints, and scale your training across the platforms’ powerful infrastructure without worrying about managing that infrastructure. Once your model is trained, you can easily deploy it as an endpoint that scales with your needs. Plus, these platforms help with monitoring, versioning, and even automating retraining so your models stay relevant. In short, ML platforms make it a lot easier for businesses to build and manage AI without getting lost in the technical details.

Key Differences

• Maturity: Amazon SageMaker has been around longer, so it’s slightly more polished and well-established. It offers a more comprehensive feature set that has been iterated upon for years, giving it an edge in terms of stability and adoption. On the other hand, Azure AI Studio is newer and introduces fresh and intuitive interfaces; however, users sometimes encounter bugs or unexplained operational failures. Of course, these issues may diminish as the platform matures.
• Ease of Use: SageMaker can feel complex and overwhelming, especially for beginners. While SageMaker’s abstracted resource creation is convenient, it’s easy to lose track of the resources you’ve spun up and what you’re being billed for. For example, it’s not uncommon to accidentally leave notebook instances or endpoints running longer than necessary, unexpectedly inflating costs. Deleting resources in SageMaker can also be tedious, requiring multiple steps and removing dependent resources. Azure AI Studio, on the other hand, offers a cleaner, more user-friendly interface. Microsoft’s most recent roll-out offers a clean, simple, and intuitive interface that makes it easy to set up, track, and manage resources efficiently.

Overall Assessment

Both platforms are powerful, but they can be costly and clunky. The extensive features and opinionated, structured workflows, tools, and pipelines may frustrate users, limiting adoption beyond prototyping and experimentation. Many companies only use individual features, like notebooks, and avoid fully committing. While they’re great for testing ideas and deploying simpler models, they require careful management and a solid understanding of the platform for more complex use cases.
I believe cost is the most important factor to consider when choosing between these options. However, comparing pricing for such complex systems can prove challenging, as the overall cost depends on the specific features you use, plus additional variables like the size of the instances and the volume of data processed. However, one thing is clear: these platforms can get very expensive, especially as you scale, so cost consideration should be a top priority.

Pre-built AI Models and APIs

To understand pre-built model services, it helps to point to a famous example: ChatGPT. ChatGPT is a pre-built large language model (LLM) wrapped in a convenient, easy-to-use API that enables a direct user interface. Even without knowing how the model was trained, users can send it some text, and ChatGPT responds with human-like conversations.

This is just one type of pre-built service; there are many others. For instance, some models can convert text to speech, turn speech into text, recognize and identify objects in images, or even translate languages in real-time. These pre-built models make it easy for developers to integrate AI capabilities into their apps without building entirely new models themselves.

Azure and AWS offer various pre-built model services, simplifying the addition of AI to your applications. AWS provides services like Amazon Polly for text-to-speech, Amazon Transcribe for speech-to-text, and Amazon Rekognition for image and video analysis. Similarly, Azure offers its own suite of pre-built models through Azure Cognitive Services. For example, Azure Speech can handle text-to-speech and speech-to-text tasks, while Azure Computer Vision is great for image recognition and analysis. These services are all API-driven, meaning you can easily plug them into your applications and start using AI almost instantly. Whether you’re looking to enhance customer experiences with chatbots, add voice recognition, or analyze images and videos, both AWS and Azure have you covered with robust, ready-to-use solutions.

Key Differences

In this case, we must compare multiple services instead of only one. Both Azure and AWS offer their own spin on the common model features—speech-to-text, text-to-speech, computer vision, translation, and so on. While some have more niche models that the other might not offer, for most cases, both platforms have you covered.
That said, there are two important differences worth mentioning:

• User Experience: For some, AWS naming conventions can be confusing or frustrating. For instance, Azure calls its text-to-speech service simply “Text to Speech”— it’s clear and to the point. AWS, on the other hand, has named theirs Polly, a reference that may be lost on some users, and downright confusing to others. Beyond naming, Azure’s overall presentation is more organized. Everything is in one place, with easier navigation and a streamlined user interface.
• Customization: Unfortunately, platforms that are easier to use often offer less flexibility. That’s certainly the case with Azure’s AI services. They’re very user-friendly, but they lack many options for customizing models to suit your needs. Even though both platforms offer managed services where a lot is abstracted away, AWS tends to give users more control and customization, a big advantage for businesses that need high levels of flexibility.

Overall Assessment:

If you know exactly what you need—for instance, a text-to-speech service—it is worthwhile to try both Azure and AWS to find the best fit. You might find that one performs better than the other for your specific use case. For example, you might prefer Polly’s more natural-sounding voice quality. Just remember to keep an eye on the costs. The pricing models for these options can be similar in some areas and different in others, so make sure to run the numbers before you fully commit.

Generative AI

While the services compared so far are often quite similar, when it comes to Generative AI offerings, the differences are more noticeable. First, Microsoft invested billions to integrate OpenAI’s models into Azure, launching what is now known as Azure OpenAI. Amazon followed closely with Amazon Bedrock. Both platforms offer deep integrations with other services and provide Retrieval-Augmented Generation (RAG) features and agents, but their operations have nuanced differences.

Key Differences

• Models: Azure OpenAI provides exclusive access to OpenAI’s flagship models, such as GPT, Codex, and DALL-E. These models are widely recognized as industry leaders in their respective domains—language generation, code synthesis, and image creation. Amazon Bedrock, on the other hand, offers a diverse selection of foundation models from multiple sources, including Anthropic, AI21, Stability AI, and Amazon’s own Titan models.
• Integrations: Both platforms integrate well with their own ecosystems and third-party services, but Azure’s integrations tend to be more streamlined and “out-of-the-box.” This simplifies rollout and deployments.
• Momentum: Currently, both services are evenly matched in terms of capabilities, but Microsoft’s partnership with OpenAI gives it a clear edge. OpenAI is a market leader in generative AI, with rapid innovation and frequent releases of new features and APIs. As Microsoft continues to integrate these capabilities into Azure, it will likely keep Azure OpenAI ahead in terms of the latest advancements. This momentum is critical when considering long-term strategy, as new capabilities are often available on Azure before making their way to AWS.

Overall Assessment:

AWS offers more flexibility but requires more initial setup effort. In AWS, having more technical expertise pays off, allowing for a more customizable experience. Essentially, Azure may be easier to use, while AWS provides greater control and flexibility.
Choosing between the two often depends on each model’s specific features. If your use case demands a model that excels in a specific area, you’ll likely choose the platform that better meets your needs.
For more advanced use cases, Azure OpenAI edges out the competition, primarily due to the strength and leadership of OpenAI in the generative AI space. I expect Azure will continue to receive new capabilities and APIs faster, making it the better long-term bet for staying ahead of the curve.

Maturity

AWS tends to be more mature and polished across most services. If reliability and stability are critical, AWS provides both. You’re less likely to experience unexpected system failures or UI bugs.

Ease of Use

On the other hand, I really appreciate how Azure structures things. One example is its use of resource groups. This feature, while unrelated to AI, makes a big difference in managing services. Everything created in Azure is organized into specific resource groups, making it easy to track, manage, and delete once you’re done. This small detail helps avoid a lot of hassle.

Flexibility

With Azure, you often get a more streamlined experience, but at the cost of fewer customization options. In other words, Azure’s simplicity comes at the cost of some flexibility, whereas AWS typically offers more control over the behaviors and interactions of deployed services.

Ecosystem

For AI services, both platforms cover around 90% of use cases. Unless you need a very specific model or feature, either platform will likely meet your needs. However, for infrastructure like data warehouses, databases, or even CI/CD services, more noticeable differences emerge. Often, the decision is less about the AI services themselves and more about how well the overall ecosystem supports your specific use case.

In general, Azure AI services are generally better integrated into their ecosystem, with noticeable examples such as Office 365, GitHub, and Azure DevOps.

Pricing

Pricing, a complex topic worthy of its own entire blog post, can often be the deciding factor. Be sure to analyze the costs thoroughly before committing. If you’re unsure, seek expert help. Many organizations overlook this critical step and end up regretting it!

Summary Table:

Written by: Dan Cohen, TeraSky Director of Cloud & DevOps