GCP vs. AWS for AI Workloads: A 2024 Comparison
An in-depth analysis of the two leading cloud platforms for artificial intelligence and machine learning.
Choosing the Right Cloud for AI
Choosing the right cloud provider is critical for success in AI and machine learning. Both Google Cloud Platform (GCP) and Amazon Web Services (AWS) offer a robust suite of tools, but they have different strengths. This post compares them across several key dimensions for AI workloads.
Vertex AI vs. SageMaker
GCP's Vertex AI is a unified MLOps platform designed to simplify the entire machine learning lifecycle. Its key advantage is the seamless integration from data ingestion (with BigQuery) to model training (with AutoML) and deployment. AWS SageMaker is more of a toolkit, offering a broader array of individual services. While powerful, it can feel less integrated than Vertex AI, sometimes requiring more effort to connect the different stages of the ML pipeline.
Specialized Hardware
Google's Tensor Processing Units (TPUs) are custom-built for AI and provide a significant performance advantage for training large models, especially in frameworks like TensorFlow. While AWS offers powerful GPU instances, TPUs on GCP often provide better performance per dollar for specific AI tasks.