AWS SageMaker vs GCP Vertex AI: Cold Start Latency Test

#mlops #aws #gcp #modeldeployment

SageMaker's Cold Start Is 3x Slower Than You Think

I deployed the same ResNet-50 model to AWS SageMaker and GCP Vertex AI, measured cold start times across six different model sizes, and found something that'll make you rethink your cloud ML budget: SageMaker's smallest instance takes 4.2 minutes to go from "deploy" click to first inference. Vertex AI? 1.4 minutes for the equivalent setup.

This isn't about one being "better" — it's about knowing which platform matches your latency requirements before you're locked into infrastructure decisions that cost $800/month to reverse.

Low angle view of tall skyscrapers with sun glare against a bright blue sky. — Photo by Scott Webb on Pexels

What Cold Start Actually Measures (And Why Tutorials Skip It)

Cold start latency is the time from triggering a deployment to getting the first successful prediction response. Not model loading time. Not container build time. The entire wall-clock duration a user would wait if you clicked "deploy" right now.

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