Developer AI Tools: AWS, Google, Microsoft

Written by
Last updated on:
October 1, 2025
Written by
Last updated on:
October 1, 2025

When we talk about artificial intelligence, something that comes immediately to our mind is where we are going to store and compute the large amounts of data and the algorithms that we will need to build accurate models for our AI solution. Amazon AWS, Microsoft Azure, and Google Cloud provide us with a set of tools and services that help us in this process. 

The main idea of this post is to give a general overview of the platforms and compare them. All of them provide tools that you can use in the whole process of implementing AI.

Amazon AWS

SageMaker Studio is a complete tool from Amazon where you can start implementing (prepare, build, train, manage, and deploy) machine learning models. SageMaker provides visual editors, debuggers, profiles, and CI/CD for machine learning. They offer solutions that manage complex machine learning infrastructure for you.

These are the tools from AWS that can improve your ML implementation speed:

  • SageMaker Data Wrangler helps to transform raw data into features. These features can later be used to make low-latency predictions based on the models. Normally, transforming raw data into features can take over 80% of model development time. According to Amazon, using data wrangler, you can reduce that time to just a fraction of the time that it typically takes.
  • SageMaker Feature Store allows you to store, update, retrieve, and share machine learning features easily with your team.
  • SageMaker Clarify identifies potential bias in your training data. When you detect bias, you can make your dataset more accurate to represent the model’s use case.
  • SageMaker Debugger aids in the detection of errors.
  • The Sage maker pipeline will provide CI (Continuous Integration) and CD (Continuous Deployment).
  • SageMaker Notebooks are an implementation of JupyterNotebooks. They are like text editors where you can run Python code, show images, text, graphs, etc.

Amazon Augmented AI (Amazon A2I) is an innovation that Azure and Google still don't have. This tool provides a way to improve the accuracy of models' predictions with the possibility to use teams of people to improve them and obtain more precise results. 

Microsoft Azure

Microsoft Azure also provides a no-code experience with the integration of different applications that Microsoft already has for businesses (Power BI, Power Apps, Power Automate, Power Virtual Agents). This can help different users from your company to take advantage of AI with tools that are friendly and ready to use.

Microsoft Automated ML allows users to create models with no-code experience by helping them train and find the best one for their data. It will provide you with good suggestions and improve interactively.

With the Microsoft Azure Machine Learning Designer, the user can create models from templates from different ones that are commonly used in IA with a drag and drop feature. It also allows you to add code in parts of the implementation to customize your models. The solution adapts to your needs.

Google Cloud Platform

Google Cloud Platform includes a suite called Google Cloud AutoML for users with no experience and minimal effort. Google helps to optimize your time because it is not necessary to implement a model or to create the infrastructure. The time for training data is also reduced to a fraction of what it usually takes using other managed solutions.

Google also provides similar tools to Amazon and Microsoft to build machine learning solutions. You can customize your experience by starting from scratch, or you can use Google's set of tools to assist you at every step of the way.

Artificial Intelligence Services Ready to Use

If you want to start with a simple integration using an SDK from various providers, you can use these available tools:

Chatbots

Translation

Speech to text

Text to speech

Text Analytics

Document analysis

Automate Image and Video Analysis

Conclusions

All of these tools provide valuable functionality to users who do not have knowledge of AI. They are compatible with a wide range of popular libraries and frameworks, including TensorFlow, MXNet, Keras, Pytorch, Chainer, SciKit Learn, and others. In addition, Microsoft and Amazon offer solutions to easily use Jupyter notebooks. In Google Cloud, you can work with them, but it will require additional configuration. They all allow you to start from scratch, build your own infrastructure and processes, or use their automated suites. The three companies have different areas of expertise. So, depending on the type of AI project, you should compare the benefits and prices. There is no general rule when choosing an AI cloud service provider as the choice depends on several factors and needs, but this blog post gives you a general idea of the tools that they offer.

Learn more

AI, Data, And Analytics

Frequently Asked Questions

AWS, Google Cloud, and Azure all provide tools for building and deploying AI models, but they focus on different strengths. AWS emphasizes end-to-end machine learning workflows through SageMaker, Google Cloud offers AutoML for fast, low-code solutions, and Azure integrates seamlessly with Microsoft’s business tools while supporting both no-code and customizable AI development.

Amazon SageMaker is an all-in-one environment for preparing, training, deploying, and managing models. It includes:

  • Data Wrangler to transform raw data into usable features
  • Feature Store to store and share features across teams
  • Clarify to detect bias in datasets
  • Debugger for tracking and resolving training errors
  • Pipelines for CI/CD automation
  • Notebooks for experimentation
    It also offers Amazon Augmented AI, a unique feature that allows human input to improve model accuracy.

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Microsoft Azure provides tools designed for both developers and non-developers. Automated ML enables users to build and train models without writing code, while the Machine Learning Designer uses a drag-and-drop interface with customizable templates. Azure also connects with tools like Power BI, Power Apps, and Power Automate, making it easier for business teams to adopt AI.

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Google Cloud AutoML is ideal for users with limited machine learning experience. It simplifies model training and deployment by handling infrastructure setup automatically and reducing training time. Developers can also start from scratch or customize advanced workflows using Google’s broader suite of AI tools.

It depends on your goals, resources, and team expertise:

  • AWS is best for customizable, end-to-end workflows.
  • Google Cloud is ideal if speed and ease of use are priorities.
  • Azure is a strong choice if your organization already relies on Microsoft products.
    Each platform supports popular frameworks like TensorFlow, PyTorch, and Keras, making them adaptable to many use cases.

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