Choosing a cloud provider for AI workloads isn’t easy. AI projects have different needs: massive compute requirements, specialized hardware, huge datasets, and tools that can make or break your development speed. Get the platform choice wrong, and you’re stuck fighting infrastructure instead of building models.
AWS, Azure, and Google Cloud Platform all offer solid AI capabilities. Each has strengths and weaknesses that matter depending on what you’re building. Let’s break down how they actually compare for real AI workloads.
Compute, Hardware, and Performance
AI workloads are compute-intensive, relying heavily on GPUs, TPUs, and specialized accelerators.
AWS probably has the widest selection of compute options. It supports NVIDIA GPUs (A100, H100, V100, T4) across multiple EC2 instance families (P, G, and Inf series). AWS also provides custom silicon such as Inferentia and Trainium, designed for cost-efficient inference and training with deep learning frameworks like TensorFlow and PyTorch via AWS Neuron SDK. This breadth makes AWS highly flexible for diverse AI workloads.
Azure also offers powerful GPU support via NVIDIA A100, H100, and older V100 GPUs through its ND and NC series. Azure has invested heavily in AI supercomputing, most famously for OpenAI models, and provides an important deal of tight integration with the NVIDIA CUDA ecosystem. Although Azure does not have any direct equivalent of Trainium, the offering for GPU availability makes it strong, thus providing enterprise-grade reliability.
GCP differentiated itself through TPUs (Tensor Processing Units), which are customized accelerators first optimized for TensorFlow and increasingly so for PyTorch with XLA. TPUs (v4 and v5e) excel at large-scale training and high-throughput inference. GCP also supports NVIDIA GPUs, but its hardware story is most compelling for teams that can leverage TPUs efficiently.
Core AI Building Blocks
AWS SageMaker is a very mature end-to-end ML platform service. It supports data labeling, training, parameter search, model deployment, monitoring, and MLOps pipelines. SageMaker also seamlessly supports popular ML frameworks, offering fine-grained control, which appeals to experienced ML engineers.
Azure Machine Learning focuses on the enterprise workflow, governance, and DevOps. It provides great support for experiment tracking, model registration, and CI/CD in Azure DevOps, GitHub, and many other features. Azure ML is commonly admired for its systemic nature of MLOps in a compliance-intensive environment.
GCP Vertex AI focuses on simplicity and performance. It unifies AutoML and custom training into a single platform and integrates deeply with BigQuery and Dataflow. Vertex AI’s managed datasets, feature store, and pipelines are intuitive, particularly for data-centric teams.
AI APis and Pretrained Models
AWS AI Services include Rekognition (vision), Comprehend (NLP), Transcribe, Translate, and Bedrock for foundation models. AWS Bedrock provides access to multiple foundation models (Anthropic, Meta, Amazon Titan) with customization options.
Azure Cognitive Services are deeply integrated with Azure OpenAI Service, offering access to GPT models, embeddings, and multimodal capabilities with enterprise security and compliance.
GCP AI APIs (Vision, Speech-to-Text, Natural Language, Translation) are known for strong accuracy, particularly in speech and vision, and integrate seamlessly with Vertex AI.
Data Engineering and Pipelines
AWS offers S3, Glue, EMR, Redshift, and Kinesis, forming a highly modular but complex data ecosystem.
Azure provides Data Lake Storage, Data Factory, Synapse Analytics, and tight integration with Power BI, making it attractive for enterprise data warehouses.
GCP shines with BigQuery, a serverless, highly scalable data warehouse that integrates natively with Vertex AI and Dataflow.
Data Storing and Management
AWS S3 is the gold standard for object storage. It’s mature, reliable, and integrates with everything. For structured data, AWS offers managed databases, data lakes, and warehousing solutions that work well with ML pipelines.
Azure Blob Storage competes well with S3, and Azure’s data stack is strong for enterprises. If your data already lives in Microsoft’s ecosystem, SQL Server, Dynamics, Office 365, Azure makes it easy to feed that into ML models. Their data integration tools are particularly good for companies with complex existing data infrastructure.
GCP‘s BigQuery is phenomenal for analytics on huge datasets. It’s fast, scales automatically, and integrates naturally with machine learning workflows. If your AI project involves processing and analyzing massive amounts of structured data before training models, BigQuery gives GCP a real edge. Their data tools feel more cohesive and easier to use than AWS’s fragmented options.
Framework and Tool Support
What frameworks and tools you use affects which platform makes sense. All three support major frameworks like TensorFlow, PyTorch, and scikit-learn. But some combinations work better than others. TensorFlow on GCP feels most natural since Google created both. PyTorch works everywhere but has particularly strong tooling on AWS through SageMaker.
If you’re using Hugging Face models, all three platforms support them, but AWS has the deepest integration through SageMaker. For MLflow and open-source MLOps tools, they run anywhere but Azure’s integration is strongest.
Kubernetes-based deployments work on all three platforms. GKE on GCP is generally considered the best-managed Kubernetes service, which matters if you’re deploying models in containers at scale.
Final Word
There’s no universally best option. Your choice depends on your specific situation. The cloud provider matters less than understanding your workload requirements and choosing deliberately. Any of these platforms can support serious AI projects. The real work is in building good models and systems, not in the underlying infrastructure. Pick one that gets out of your way and lets you focus on that.