Amazon SageMaker and Google’s Gemini Enterprise Agent Platform (the current name for what was Vertex AI, and before that AI Platform) both offer end-to-end machine learning workflows, managed notebooks, prebuilt algorithms, and AutoML. The real differences show up in hardware options, pricing structure, and how each fits your team’s existing stack.
Platform Overview
The Gemini Enterprise Agent Platform handles everything from data prep to model serving, with managed notebooks supporting TensorFlow, PyTorch, or scikit-learn and AutoML for building models without heavy coding. SageMaker covers the same ground through SageMaker Studio, an integrated environment for notebooks, training, and deployment, with its own set of built-in algorithms for common tasks.
Hardware and Scaling
AWS’s current training hardware runs on the EC2 P5 family (NVIDIA H100 and H200 GPUs), plus AWS Trainium for purpose-built large-model training. Google Cloud offers NVIDIA GPU options alongside Ironwood, its current-generation TPU built primarily for large-scale inference. Both platforms auto-scale inference endpoints, SageMaker can scale down to zero instances between requests, and the Gemini Enterprise Agent Platform lets you define minimum and maximum node counts for the same effect.
Pricing
Both bill pay-as-you-go: Google Cloud charges per second with a one-minute minimum across the board, while AWS bills Linux instances per second but Windows and GPU instances on a one-hour minimum. Both offer one- and three-year committed-use discounts, and spot (AWS) or preemptible (Google Cloud) instances for interruptible workloads at a steep discount, full breakdown in our Google Cloud vs AWS pricing comparison.
Security and Ecosystem
Both platforms encrypt data at rest with customer-managed keys and use TLS in transit, and both hold the major compliance certifications (ISO 27001, SOC 2, HIPAA), check each provider’s current documentation for the specifics that matter to your workload. On the ecosystem side, Google Cloud ties naturally into BigQuery, Cloud Storage, and Dataflow, while AWS integrates S3, Redshift, and Glue for ETL, and both support Kubeflow and MLflow for teams that want to stay tool-agnostic.
Choosing Between Them
Team familiarity is often the deciding factor in practice, if your engineers already know TensorFlow and the broader Google ecosystem, the Gemini Enterprise Agent Platform will feel more natural; teams already living in the AWS console tend to move faster in SageMaker. Beyond that, run a small pilot on both and compare real training and inference costs for your actual workload rather than estimating from list prices alone. See our Google Cloud vs AWS certification comparison if you’re also weighing which platform to build formal skills in.