AI Infrastructure Across Cloud Providers: GPUs, Managed ML Platforms and Foundation Models
What AI and machine learning workloads need from a cloud platform, and how AWS, Google Cloud, Azure and Alibaba Cloud each provide GPU compute, managed ML platforms and hosted foundation models.
AI and machine learning workloads need more than a general-purpose server. The full lifecycle, from data preparation and feature engineering through training, deployment and monitoring, draws on GPU instances, high-memory compute, fast storage for datasets and checkpoints, and managed services that stitch the steps together. Increasingly it also means calling hosted foundation models rather than training your own. This guide explains what those needs are and how the major providers meet them.
What AI workloads need #
| Need | Why it matters |
|---|---|
| GPU (and TPU) instances | Training and inference for deep learning are parallel workloads that run orders of magnitude faster on accelerators |
| High-memory compute | Large models and feature pipelines need memory-optimized machines even when a GPU is not involved |
| Fast storage | Datasets and checkpoints are read and written repeatedly; slow storage starves expensive accelerators |
| Managed ML platforms | Notebooks, experiment tracking, training jobs, model registries and endpoints without building the plumbing |
| Hosted foundation models | Generative AI features through an API, without running the model infrastructure |
| Prebuilt AI services | Vision, speech and language APIs for teams that do not need custom models |
AWS #
Amazon EC2 offers GPU instance families for training and inference, and Amazon SageMaker is the managed platform for building, training and deploying models at scale. Amazon Bedrock provides foundation models from several AI companies through a single API, with options to experiment across models, fine-tune for specific use cases and scale without managing infrastructure. Prebuilt services cover vision (Rekognition), language (Comprehend), speech (Polly, Transcribe) and conversational interfaces (Lex). Our AWS AI services guide and Bedrock overview go deeper.
Google Cloud #
Compute Engine offers GPU machine types and Google’s own TPUs. Vertex AI is the unified platform to train and deploy models, tune large language models and build AI applications. Model Garden gives access to more than 200 models, including Google’s Gemini family, Anthropic’s Claude and open models such as Llama, so teams can pick the right model per use case. BigQuery and Dataflow handle the data side.
Microsoft Azure #
Azure provides GPU virtual machine sizes and Azure Machine Learning for the end-to-end lifecycle from data preparation to deployment and monitoring. Azure OpenAI Service hosts large language models for generative applications, AI Foundry brings model selection and app building together, and Azure AI services provide prebuilt vision, speech and language capabilities. Our Azure AI overview covers the catalog.
Alibaba Cloud #
Alibaba Cloud’s Platform for AI (PAI) covers the development cycle from labeling and modeling to training, optimization and deployment. Model Studio (Bailian) lets developers build generative applications on the Qwen family, including Qwen-Max and the multimodal Qwen-VL, with workloads running in isolated VPC environments. Qwen models are also among the most downloaded open-source large language models, which makes them common on other platforms too. Alibaba Cloud has offered new users a substantial allowance of free model tokens under its trial terms.
Developer clouds #
Linode offers NVIDIA GPU instances for machine learning, inference, scientific computing and video processing, and DigitalOcean offers GPU Droplets. These suit teams that want raw accelerators with a simpler console and pricing, and are happy to run their own tooling on top.
Choosing between them #
- You want hosted models through an API: Bedrock, Vertex AI Model Garden, Azure OpenAI or Model Studio, depending on which models and regions you need.
- You train custom models: SageMaker, Vertex AI, Azure Machine Learning or PAI, choosing the platform whose data services you already use.
- You need specific accelerators: compare instance availability by region; GPU capacity is constrained everywhere and quotas start low on new accounts.
- You want simplicity: a GPU instance on a developer cloud with your own framework.
Cost notes #
Accelerators are the most expensive compute you will rent. Use Spot or preemptible capacity for interruptible training, stop notebooks and endpoints when idle, keep datasets in object storage with lifecycle rules, and put a budget alert on the AI project before the first training run. Our pricing models comparison explains the commitment and spot options.
Where the data lives decides a lot #
Moving large datasets between clouds is slow and, on hyperscalers, billed as outbound traffic. Teams usually get better results by running AI workloads on the platform that already holds their data and identity setup, then calling models on another provider only when a specific model justifies it. Region availability for accelerators and hosted models varies, so check the region list before committing to a design.
A typical AI project stack #
| Stage | What runs | Typical services |
|---|---|---|
| Data preparation | Ingestion, cleaning, labeling, feature engineering | Object storage, data warehouses, labeling tools in the ML platform |
| Training | Experiments and training jobs on accelerators | GPU or TPU instances, managed training jobs, experiment tracking |
| Evaluation | Comparing models against held-out data | Model registry, evaluation tooling |
| Deployment | Serving predictions or generations behind an API | Managed endpoints, foundation model APIs, serverless functions |
| Monitoring | Latency, cost, drift and quality | Platform monitoring, logging and cost tools |
Hosted foundation models collapse the first three stages for many applications: you skip training and evaluate prompts instead. Custom models still need the full pipeline, which is where the managed ML platforms earn their keep.
Frequently asked questions #
Which cloud is best for AI development?
The one that hosts the models you want to call and the data you already have. AWS, Google Cloud and Azure all offer GPU compute, a managed ML platform and hosted foundation models; Alibaba Cloud is strongest for Qwen and Asian regions.
Do I need to train my own model?
Often not. Hosted foundation models with prompt engineering or light fine-tuning cover many applications. Train custom models when you have proprietary data and a task the hosted models handle poorly.
Why can I not launch a GPU instance on a new account?
New accounts have low default quotas for GPU instance types. Request an increase through the provider’s quota process; approval usually follows some billing history.
