Amazon SageMaker and Google’s Gemini Enterprise Agent Platform (the rebranded, expanded successor to Vertex AI as of Google Cloud Next 2026) 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. The rebrand wasn’t just cosmetic: model training, AutoML, the Model Registry, and prediction endpoints are now positioned as sub-features under an agent-first platform rather than the other way around, though existing API integrations kept working unchanged through the transition. 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 training hardware has moved a generation since this comparison was first written. EC2 P6-B200 instances, powered by NVIDIA’s Blackwell B200 GPUs, are now generally available and offer roughly double the training throughput of the previous P5en generation, with 1,440 GB of pooled high-bandwidth GPU memory across an 8-GPU instance. For teams training very large models on AWS’s own custom silicon rather than Nvidia GPUs, Trainium2-based Trn2 instances remain the option built specifically for hundred-billion-to-trillion-parameter workloads. Google Cloud’s current flagship is Ironwood (TPU7x), its seventh-generation TPU, built primarily for inference at scale: 4.6 petaFLOPS per chip, 192 GB of high-bandwidth memory per chip, and a claimed 42.5 exaFLOPS across a full 9,216-chip pod. Google has already previewed an eighth generation split into separate training- and inference-optimized chips, so this is a genuinely fast-moving part of the comparison on both sides, not a settled picture. 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.
CUDA Portability vs. Custom Silicon Lock-In
This is the part of the decision that’s easy to underweight next to raw FLOPS numbers. AWS’s GPU instances (P5, P6-B200) run standard CUDA, so a training pipeline built on them ports to another CUDA-based cloud, or back to on-prem GPUs, with comparatively little rework. Trainium and TPUs are both proprietary accelerator architectures with their own compiler toolchains (Neuron SDK for Trainium, XLA/JAX or PyTorch/XLA for TPUs); code tuned specifically for either one doesn’t move to a different vendor’s hardware without real re-engineering. That’s a fine trade if the cost or performance advantage is large enough and the team is committed to one cloud long-term, but it’s a genuine lock-in cost that a pure price-per-FLOP comparison misses, and it’s worth deciding deliberately rather than discovering it later when a migration is already under consideration.
Pricing
Both bill pay-as-you-go: Google Cloud charges per second with a one-minute minimum across the board, and AWS bills per second too, with a 60-second minimum that applies across Linux, Windows, and GPU instances alike since AWS extended per-second billing to Windows and SQL Server in 2021. 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, while teams already living in the AWS console tend to move faster in SageMaker. Where the hardware question above actually matters is training cost at scale, if a model is large enough that custom silicon (Trainium or TPU) meaningfully beats general-purpose GPU pricing for your specific workload, and only real benchmarking on your own training job settles that, not list prices. 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.