Key Takeaway
Platform selection should follow your data, not your compute preferences. The platform where your core datasets already reside typically offers the lowest total friction, even if another platform has superior individual services.
Why Platform Selection Is Consequential
Choosing the right AI platform is a consequential decision with multi-year implications for your engineering velocity, cost structure, and talent strategy. Migration between platforms is expensive -- not just in infrastructure cost, but in rewriting training pipelines, revalidating model performance, retraining your team, and rebuilding operational runbooks.
This guide provides a feature-by-feature comparison of AWS (SageMaker, Bedrock), Azure (Azure AI Studio, Azure ML), and Google Cloud (Vertex AI) supplemented by analysis of platform-agnostic alternatives. Rather than declaring a winner, it maps platform strengths to organizational profiles so you can identify the best fit for your specific context.
Platform Comparison Matrix
The following comparison covers the eight evaluation categories that matter most for production AI workloads. Each rating reflects the platform's strength relative to the other two major cloud providers.
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