Several cloud services support AI deployments, each offering a range of tools for training, hosting, and scaling models. At AEHEA, we often guide clients through selecting the right cloud platform based on their goals, budget, and the complexity of the AI system they’re building. Some platforms offer prebuilt solutions while others give full infrastructure control, depending on how much flexibility you need.

Amazon Web Services (AWS) is one of the most robust platforms for AI work. It provides EC2 instances with GPU acceleration, S3 storage for datasets, and managed services like SageMaker for training and deploying models. SageMaker simplifies the workflow by offering built-in Jupyter environments, automatic model tuning, and scalable endpoints for inference. AWS is a solid choice if you need advanced customization or enterprise-grade infrastructure.

Google Cloud Platform (GCP) is another leader in the space, especially if you want to work with natural language, translation, or image recognition. Their Vertex AI service provides tools to build, deploy, and monitor models, whether you’re using AutoML or custom TensorFlow and PyTorch code. GCP also integrates tightly with the Hugging Face ecosystem and provides TPU acceleration, which is useful for specific deep learning tasks.

Microsoft Azure offers a well-integrated set of AI tools under its Azure Machine Learning service. It supports both low-code and custom development, model versioning, and collaborative model training environments. Azure also provides strong access control and compliance options, which is especially helpful in regulated industries like finance or healthcare. You can deploy models using Docker containers, notebooks, or directly through API services.

Beyond the big three, there are specialized platforms like RunPod, Paperspace, and Lambda Labs, which focus specifically on GPU hosting for AI. These services are often cheaper and easier to scale for teams that just need GPU time without the overhead of full cloud environments. We sometimes use these platforms at AEHEA for training large models or running compute-heavy inference jobs outside of production.

Choosing the right service depends on how hands-on you want to be. Some teams need a serverless API with auto-scaling, while others want root access to configure every detail. What matters is finding the right balance between control, cost, and reliability for your specific AI use case.